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Cooking with Data and Processes

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Faculty of Computer Science - Free University of Bozen-Bolzano
Faculty of Computer Science - Free University of Bozen-Bolzano

Overview of our research on integrated models for data and processes, and their formal verification.

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x Cooking with data and processes Marco Montali Free University of Bozen-Bolzano credits to Andy Rivkin
2 Management [models] Workers [reality] Experience Dichotomy
3 Management Dichotomy Business [decision making] IT [infrastructure]
4 Expertise Dichotomy Master Data Management Business Process Management
5 A Successful Organization
Travel Reimbursement Financial accounting review Employee prepare travel request prepare reimbursement request request info send result Travel log data
Travel Reimbursement Financial accounting review Employee prepare travel request prepare reimbursement request request info send result Travel log data Persistent data Local, case data External inputs
General Recipe • Explicit control-flow • Local data (case perspective) • Global data (persistent storage) • Updatable global data • External inputs • Support of automated analysis “REAL” PROCESS
Why Automated Analysis? • Classical motivations • Multi-perspective process discovery • Typical approach: different perspectives mined with different techniques • A-posteriori recombination • No guarantee of overall correctness • Verification in the loop!
Formal Verification The Conventional, Propositional Case Process control-flow (Un)desired property Abstract model underlying variants of artifact-centric systems. Semantically equivalent to the most expressive models for business proc systems (e.g., GSM). Data Process Data+Process Data Layer: Relational databases / ontologies Data schema, specifying constraints on the allowed states Data instance: state of the DCDS Process Layer: key elements are Atomic actions Condition-action-rules for application of actions Service calls: communication with external environment, new data! alvanese (FUB) Foundations of Data-Aware Process Analysis INRIA Saclay Paris – 18/3/2016
(Un)desired property Finite-state transition system Propositional temporal formula|= Formal Verification The Conventional, Propositional Case Process control-flow Abstract model underlying variants of artifact-centric systems. Semantically equivalent to the most expressive models for business proc systems (e.g., GSM). Data Process Data+Process Data Layer: Relational databases / ontologies Data schema, specifying constraints on the allowed states Data instance: state of the DCDS Process Layer: key elements are Atomic actions Condition-action-rules for application of actions Service calls: communication with external environment, new data! alvanese (FUB) Foundations of Data-Aware Process Analysis INRIA Saclay Paris – 18/3/2016
(Un)desired property Finite-state transition system Propositional temporal formula|= Formal Verification The Conventional, Propositional Case Process control-flow Verification via model checking 2007 Turing award: Clarke, Emerson, Sifakis Abstract model underlying variants of artifact-centric systems. Semantically equivalent to the most expressive models for business proc systems (e.g., GSM). Data Process Data+Process Data Layer: Relational databases / ontologies Data schema, specifying constraints on the allowed states Data instance: state of the DCDS Process Layer: key elements are Atomic actions Condition-action-rules for application of actions Service calls: communication with external environment, new data! alvanese (FUB) Foundations of Data-Aware Process Analysis INRIA Saclay Paris – 18/3/2016
Formal Verification The Data-Aware Case (Un)desired property Data-aware process el underlying variants of artifact-centric systems. quivalent to the most expressive models for business process GSM). Data Process Data+Process elational databases / ontologies ma, specifying constraints on the allowed states nce: state of the DCDS key elements are tions action-rules for application of actions alls: communication with external environment, new data! Foundations of Data-Aware Process Analysis INRIA Saclay Paris – 18/3/2016 (24/1)
(Un)desired property First-order temporal formula|= Infinite-state, relational transition system [Vardi 2005] el underlying variants of artifact-centric systems. quivalent to the most expressive models for business process GSM). Data Process Data+Process elational databases / ontologies ma, specifying constraints on the allowed states nce: state of the DCDS key elements are tions action-rules for application of actions alls: communication with external environment, new data! Foundations of Data-Aware Process Analysis INRIA Saclay Paris – 18/3/2016 (24/1) Data-aware process Formal Verification The Data-Aware Case
(Un)desired property First-order temporal formula|= ? Infinite-state, relational transition system [Vardi 2005] el underlying variants of artifact-centric systems. quivalent to the most expressive models for business process GSM). Data Process Data+Process elational databases / ontologies ma, specifying constraints on the allowed states nce: state of the DCDS key elements are tions action-rules for application of actions alls: communication with external environment, new data! Foundations of Data-Aware Process Analysis INRIA Saclay Paris – 18/3/2016 (24/1) Data-aware process Formal Verification The Data-Aware Case
Why FO Temporal Logics • To inspect data: FO queries • To capture system dynamics: temporal modalities • To track the evolution of objects: FO quantification across states • Example: It is always the case that if an order is order, then that order is eventually either cancelled, or paid and then delivered • N.B.: the interplay between FO quantification and temporal modalities is subtle!
BPMNProcesses and Data The process model The data model Modeling and In-Database Management of Relational, Data-Aware Processes start Start Workflow Review Request Fill Reimb. accepted Review Reimb. End Workflow rejected end Pending ID: int empl : string dest : string Accepted ID :int empl : string dest : string cost : int Rejected ID : int empl : string dest : string TrvlMaxAmnt ID : int FID : int maxAmnt : int CurrReq ID: int empl : string dest : string status : string {submitd, acceptd, reimbd, rejd, complete} TrvlCost ID : int FID : int cost : int FK_TrvlMaxAmnt_CurrReq FK_TrvlCost_CurrReq
Processes and Data The process model The data model Modeling and In-Database Management of Relational, Data-Aware Processes start Start Workflow Review Request Fill Reimb. accepted Review Reimb. End Workflow rejected end Pending ID: int empl : string dest : string Accepted ID :int empl : string dest : string cost : int Rejected ID : int empl : string dest : string TrvlMaxAmnt ID : int FID : int maxAmnt : int CurrReq ID: int empl : string dest : string status : string {submitd, acceptd, reimbd, rejd, complete} TrvlCost ID : int FID : int cost : int FK_TrvlMaxAmnt_CurrReq FK_TrvlCost_CurrReq BPMN
Cooking with BPMN • Explicit control-flow • Local data (case perspective) • Global data (persistent storage) • Updatable global data • External inputs • Support of automated analysis TASTELESS DATA ~ ~
Real WfMS Review Request Fill Reim- bursement Review Reim- bursement Rejected Accepted
Case and Persistent Data Review Request Fill Reim- bursement Review Reim- bursement Rejected Accepted req info result reimbursement personal info
Persistent Data Engineering persistent storage Review Request Fill Reim- bursement Review Reim- bursement Rejected Accepted req info result reimbursement personal info framework data model custom code
Case Data Engineering persistent storage Review Request Fill Reim- bursement Review Reim- bursement Rejected Accepted req info result reimbursement personal info framework data model custom code user forms external services
Decision Modeling persistent storage Review Request Fill Reim- bursement Review Reim- bursement Rejected Accepted req info result reimbursement personal info framework data model custom code external services 24
Cooking with WfMS • Explicit control-flow • Local data (case perspective) • Global data (persistent storage) • Updatable global data • External inputs • Support of automated analysis DATA AS “PROCEDURAL ATTACHMENT" ~ ~
Becoming a Chef… Diets Process- centric Data- centric Balanced Modeling Enactment Verification Courses
x The Data-Centric Diet
Business Entities/Artifacts Data-centric paradigm for process modeling • First: elicitation of relevant business entities that are evolved within given organizational boundaries • Then: definition of the lifecycle of such entities, and how tasks trigger the progression within the lifecycle • Active research area, with concrete languages (e.g., IBM GSM, OMG CMMN) • Cf. EU project ACSI (completed)
Cooking with Artifacts • Explicit control-flow • Local data (case perspective) • Global data (persistent storage) • Updatable global data • External inputs • Support of automated analysis TASTELESS FLOWS ~ ~ ~
Key Foundational Results Artifact-centric systems can be formalized using Data- Centric Dynamic Systems (DCDSs) [PODS 2013] DCDSs provide: • an execution semantics based on relational transition systems • verification results when the system is studied starting from an initial, fixed instance Confront with the line of research on parameterised verification
DCDS Modeling Data layer: persistent data •Relational database with integrity constraints Process layer: evolves the data •Atomic actions: update the data •CA rules: a control-flow component that determines when actions 
 can be executed •Service calls: interact with external world, inject new data! Data-centric Dynamic Systems (DCDSs) [PODS’13] DCDS Data Layer Process Layer ... Services Data layer: maintains data of interest I Relational database with integrity constraints Process layer: evolves the data I Atomic actions: update the data I CA rules: a control-flow component that determines when actions can be executed I Service calls: interact with external world, inject new data! [PODS’13] Bagheri Hariri B., Calvanese D., De Giacomo G., Deutsch A., and Montali M. â ˘AIJVerification of Relational Data-centric Dynamic Systems with External Servicesâ ˘A˙I. In: Proc. of PODS. ACM, 2013, pp. 163â ˘A ¸S174 Modeling, Enactment and Verification of Data-Aware Processes 17 / 55
DCDS Modeling “… examine an employee’s trip request and, if accepted, assign the maximum reimbursable amount to it …” Data-centric Dynamic Systems (DCDSs) “. . . examine an employee’s trip request and, if accepted, assign the maximum reimbursable amount to it. . . ” ID Empl Dest Status 1 Bob NY submitted 2 Kriss Rome submitted CurrReq ID FID maxAmnt - – – TrvlMaxAmnt Modeling, Enactment and Verification of Data-Aware Processes 18 / 55
DCDS Modeling “… examine an employee’s trip request and, if accepted, assign the maximum reimbursable amount to it …” Data-centric Dynamic Systems (DCDSs) “. . . examine an employee’s trip request and, if accepted, assign the maximum reimbursable amount to it. . . ” ID Empl Dest Status 1 Bob NY submitted 2 Kriss Rome submitted CurrReq ID FID maxAmnt - – – TrvlMaxAmnt Modeling, Enactment and Verification of Data-Aware Processes 18 / 55 Data-centric Dynamic Systems (DCDSs) ID Empl Dest Status 1 Bob NY submitted 2 Kriss Rome submitted CurrReq ID FID maxAmnt - – – TrvlMaxAmnt CA rule: CurrReq(id, e, d, submitted) 7! REVIEWREQUEST(id, e, d) Select the request of Kriss Run REVIEWREQUEST actionExample: ReviewRequest executable for Kriss
DCDS Modeling “… examine an employee’s trip request and, if accepted, assign the maximum reimbursable amount to it …” Data-centric Dynamic Systems (DCDSs) “. . . examine an employee’s trip request and, if accepted, assign the maximum reimbursable amount to it. . . ” ID Empl Dest Status 1 Bob NY submitted 2 Kriss Rome submitted CurrReq ID FID maxAmnt - – – TrvlMaxAmnt Modeling, Enactment and Verification of Data-Aware Processes 18 / 55 Example: Data-centric Dynamic Systems (DCDSs) ID Empl Dest Status 1 Bob NY submitted 2 Kriss Rome submitted CurrReq ID FID maxAmnt - – – TrvlMaxAmnt Action REVIEWREQUEST(id, e, d) : 8 >>>< >>>: true del{CurrReq(id, e, d, submitd)} add{CurrReq(id, e, d, status(e, d)), TrvlMaxAmnt(genpk(), id, maxAmnt(e, d))} 9 >>>= >>>; status(Kriss, Rome) = accepted maxAmnt(Kriss, Rome) = 900 genpk() = 10 Update the database Data-centric Dynamic Systems (DCDSs) ID Empl Dest Status 1 Bob NY submitted 2 Kriss Rome accepted CurrReq ID FID maxAmnt 10 2 900 TrvlMaxAmnt
DCDS Modeling “… examine an employee’s trip request and, if accepted, assign the maximum reimbursable amount to it …” Data-centric Dynamic Systems (DCDSs) “. . . examine an employee’s trip request and, if accepted, assign the maximum reimbursable amount to it. . . ” ID Empl Dest Status 1 Bob NY submitted 2 Kriss Rome submitted CurrReq ID FID maxAmnt - – – TrvlMaxAmnt Modeling, Enactment and Verification of Data-Aware Processes 18 / 55 Example: Data-centric Dynamic Systems (DCDSs) ID Empl Dest Status 1 Bob NY submitted 2 Kriss Rome submitted CurrReq ID FID maxAmnt - – – TrvlMaxAmnt Action REVIEWREQUEST(id, e, d) : 8 >>>< >>>: true del{CurrReq(id, e, d, submitd)} add{CurrReq(id, e, d, status(e, d)), TrvlMaxAmnt(genpk(), id, maxAmnt(e, d))} 9 >>>= >>>; status(Kriss, Rome) = accepted maxAmnt(Kriss, Rome) = 900 genpk() = 10 Update the database Data-centric Dynamic Systems (DCDSs) ID Empl Dest Status 1 Bob NY submitted 2 Kriss Rome accepted CurrReq ID FID maxAmnt 10 2 900 TrvlMaxAmnt How to model? The Review Request action representation in dapSL RVWREQUES T(id, empl, dest): DELETE FROM CurrReq WHERE CurrReq.id = id; INSERT INTO CurrReq(id, empl, dest, status) VALUES(id, empl, dest, @status(empl, dest)); INSERT INTO TrvlMaxAmnt(tid, tfid, tmaxAmnt) VALUES(@genpk(), id, @maxAmnt(empl, dest)) Modeling, Enactment and Verification of Data-Aware Processes
DCDS Enactment DAPHNE: SQL+Java executor for DCDSs [Hopefully CAiSE 2019] • SQL-like frontend • Translation into procedural SQL stored procedures • Management of external services • Execution directly on top of the database, with different execution modalities (including state-space construction) Towards implementing RDSs A RDS model is maintained by the RDBMS (e.g., PostgreSQL) Interaction provided by the Flow Engine, which: I executes calls to the RDBMS I interplays with external services through a Service Manager Modeling, Enactment and Verification of Data-Aware Processes 21 / 55
DCDS Verification Highly-undecidable in general Attacked for “state-bounded” systems [PODS 2013] In a state-bounded system, the number of tuples accumulated in a single state cannot exceed a pre-defined bound • Still, infinitely many distinct values can be encountered within and across runs
Verification of State-Bounded DCDSs [PODS 2013, Inf. Com. 2018] Branching-time FO logics (contain reachability, soundness,..) • decidable and reducible to standard finite-state model checking
 —> hence, so are soundness properties Linear-time FO logics • Undecidable in general: the system is not Turing-powerful on its own, but the logics isolate runs and separate spurious from genuine ones • Decidable and reducible to standard finite-state model checking by controlling the interplay between FO quantifiers and temporal modalities (object persistency)
Is My System State-Bounded? • For a fixed k: decidable. • For “some” bound: undecidable. • Classes with decidable state boundedness • Reasonable for the control layer, not for the data logic [KR2014,ACSD2018,Formal Asp. Comp.2016] • Sufficient, syntactic conditions [PODS2013,KR2014] • Methodologies to guarantee state-boundedness by design [ICSOC2013,CIKM14,STTT16,Formal Asp. Comp.2016]
x The Process-Centric Diet
ν-Petri Nets
 [Rosa-Velardo and de Frutos-Escrig, Fund. Inf. 2008] Petri nets with name creation and management • Tokens carry names: ids coming from an infinite domain, can be compared for equality via arc inscriptions • ν variables indicate the generation of fresh names not present in the net • ν-PNs are WSTS —> termination, coverability, boundedness decidable
Modeling with ν-Petri Nets
 #1: multiple cases properties that predicate about the evolution of names across states, but we also implicitly isolate a class of DCDSs for which state-boundedness is decidable. This is achieved by leveraging the decidability of state-boundedness for ‹-PNs and the connection provided via the translation. Let us now comment on the modeling power of the Petri net classes presented in this chapter and show how the travel reimbursement example can be represented in di erent contexts using RIAW-nets and ‹-PNs. Get Pending Request Start Workflow Review Request Got Rejected Got Accepted Fill Reimb Review Reimb Reimburse Reject Log Accepted Log Rejected ‹ x x x x x x x x x x x x x x x x x x Financial Accounting manager ‘ ‘ ‘ Figure 5.9: RIAW-net that represents the travel reimbursement process from Section 1.4, extended with a Financial Accounting resource As we have shown in Section5.1.4, RIAW-nets contribute to the concept of Emitter (new case id generation)
Modeling with ν-Petri Nets
 #1: multiple cases properties that predicate about the evolution of names across states, but we also implicitly isolate a class of DCDSs for which state-boundedness is decidable. This is achieved by leveraging the decidability of state-boundedness for ‹-PNs and the connection provided via the translation. Let us now comment on the modeling power of the Petri net classes presented in this chapter and show how the travel reimbursement example can be represented in di erent contexts using RIAW-nets and ‹-PNs. Get Pending Request Start Workflow Review Request Got Rejected Got Accepted Fill Reimb Review Reimb Reimburse Reject Log Accepted Log Rejected ‹ x x x x x x x x x x x x x x x x x x Financial Accounting manager ‘ ‘ ‘ Figure 5.9: RIAW-net that represents the travel reimbursement process from Section 1.4, extended with a Financial Accounting resource As we have shown in Section5.1.4, RIAW-nets contribute to the concept of Emitter (new case id generation) req0 req1 req2 req3 req4 req5 req6 … …
Modeling with ν-Petri Nets
 #1: multiple cases with resources properties that predicate about the evolution of names across states, but we also implicitly isolate a class of DCDSs for which state-boundedness is decidable. This is achieved by leveraging the decidability of state-boundedness for ‹-PNs and the connection provided via the translation. Let us now comment on the modeling power of the Petri net classes presented in this chapter and show how the travel reimbursement example can be represented in di erent contexts using RIAW-nets and ‹-PNs. Get Pending Request Start Workflow Review Request Got Rejected Got Accepted Fill Reimb Review Reimb Reimburse Reject Log Accepted Log Rejected ‹ x x x x x x x x x x x x x x x x x x Financial Accounting manager ‘ ‘ ‘ Figure 5.9: RIAW-net that represents the travel reimbursement process from Section 1.4, extended with a Financial Accounting resource As we have shown in Section5.1.4, RIAW-nets contribute to the concept of Emitter (new case id generation) req0 req1 req2 req3 req4 req5 req6 … … Resource (bounds simultaneously active cases)
Modeling with ν-Petri Nets
 #2: single case with case variables te that in this case the data are abstracted away or, whenever data based diversion nts of the business process flow have to be implemented, “hardcoded” in the kflow definition (for example, Got Rejected represents the failed request approval d models the diversion point of the process in Figure 1.1). Pending Start Workflow CurrReq Submitd Review Request Rejected Review Request Accepted TrvlMax Amount CurrReq Acceptd Fill Reimb TrvlCost CurrReq Complete Review Reimb Rejd Review Reimb Reimbd CurrReq Reimbd CurrReq Rejd Log Accepted Log Rejected x x x x x ‹ ‹ x x ‹ x x x x x x x x x x x gure 5.10: ‹-PN that represents the travel reimbursement process from Section 1.4 y y y y Not very satisfactory: ν variables only useful to represent the generation of new ids
Model Ceckhing ν-Petri Nets [Formal Asp. Comp. 2016] Encoding of ν-PNs into DCDSs Bounded ν-PNs translate into state-bounded DCDSs —> An interesting class of DCDSs with decidable state- boundedness <— Verification against FO temporal logics
Data Petri Nets [Mannhardt PhD Thesis 2018] • Petri nets with external (case) variables • Each variable has a datatype (with possibly infinite domain) • Transitions read and write variables • Boolean formulae used to express • guards • constraints on value insertion • In [ER 2018] we argued that DPNs elegantly capture decision- aware process models with DMN tables [Batoulis et al, ER 2017] D M Ndiscoverable
DPN ExampleM. de Leoni, P. Felli, M. Montali i credit request [amountw ≥ 0] p1 verify [okw ] renegotiate request ! amountr > 15000 ∧ okr == false " skip assessment ! okr == false " p2 simple assessment ! okr == true ∧ okw ∧ amountr < 5000 " advanced assessment ! okr == true ∧ okw ∧ amountr ≥ 5000 " p3 AND split p5p4 open credit loan [okr == true] inform acceptance customer VIP inform acceptance customer normal ! okr == true ∧ amountr < 10000 " inform rejection customer VIP ! okr == true ∧ amountr ≥ 10000 "! okr == false ∧ amountr ≥ 10000 " p7p6 AND join o 1. Our working example of a DPN. Writing operations exist every time guards mention Managing a request with 2 case variables
Soundness of DPNs
 with variable-constant conditions [ER 2018] Definition of data-aware soundness (soundness with “some” variable assignment) Data abstraction technique that preserves soundness • Consider representative values instead of actual values • There are finitely many constants used in the net, so representatives are finite • Propositionalize the data and analyze the resulting net Recently extended to variable-variable conditions, using constraints instead of representative values
• Implementation in CPN Tools / ProM 1. Translate the DPN into a CPN • Each variable becomes a coloured place • The place always contains a single token tracking the variable value • Read-write operations —> outgoing/incoming arcs 2. Fix the domain of a variable place to contain only its finitely many representatives —> bounded CPN! Soundness of DPNs
 with variable-constant conditions [ER 2018] Soundness Verification of Decision-Aware Process Models 9 i A [xw ] p1 B [xr > 10] C [xr ≤ 10] Fig. 2. Conversion of a simple DPN to CPN (left to right). The green token represents a token with value 0. Arcs without annotations are considered annotated by v• and places with no color are associated with •. Double-headed arcs stand for two arcs with same inscription in both directions.
• Implementation in CPN Tools / ProM 1. Translate the DPN into a CPN • Each variable becomes a coloured place • The place always contains a single token tracking the variable value • Read-write operations —> outgoing/incoming arcs 2. Fix the domain of a variable place to contain only its finitely many representatives —> bounded CPN! Soundness of DPNs
 with variable-constant conditions [ER 2018] Soundness Verification of Decision-Aware Process Models 9 i A [xw ] p1 B [xr > 10] C [xr ≤ 10] Fig. 2. Conversion of a simple DPN to CPN (left to right). The green token represents a token with value 0. Arcs without annotations are considered annotated by v• and places with no color are associated with •. Double-headed arcs stand for two arcs with same inscription in both directions. Soundness Verification of Decision-Aware Process Models
• Explicit control-flow • Local data (case perspective) • Global data (persistent storage) • Updatable global data • External inputs • Support of automated analysis Cooking with PNs and Cases MISSING KEY INGREDIENTS… ~
What about Multiple Cases? • Each case has its own variables • Infinitely many cases may be seen over time • So each case has to carry its own variables! —> Coloured Petri Nets were each 
 “case token” carries a tuple of values • No clear representation of “external” inputs • CPNs usually studied with finite color domains • With unbounded color domains: everything becomes undecidable
• Explicit control-flow • Local data (case perspective) • Global data (persistent storage) • Updatable global data • External inputs • Support of automated analysis Cooking with CPNs STILL MISSING KEY INGREDIENTS… ~ ~
x The Balanced Diet
DB-Nets
 [ToPNoC 2017] Relational DB with constraints Persistence layer “basic” CPN Control layer
DB-Nets
 [ToPNoC 2017] Relational DB with constraints Persistence layer “basic” CPN Control layer Data logic layer query action
DB-Nets
 [ToPNoC 2017] Relational DB with constraints Persistence layer “basic” CPN Control layer SQL/DCDSquery action view place Data logic layer
DB-Nets
 [ToPNoC 2017] Relational DB with constraints Persistence layer “basic” CPN Control layer SQL/DCDSquery action view place a(x,y) action-bound trans. X Data logic layer
Example Emp name: string Ticket id: int descr: string Resp emp: string ticket: int Each employee can handle at most one ticket at a given time Log ticket: int emp: string descr: string
Example: Queries Get tickets and their description Get “idle” employees plying the update on the given database in over deletions (this is a standard approach situation in which the same fact is asserted The data logic simply exposes a set of q be used by the control layer to obtain data induce updates on the persistence layer. Definition 14 (Data logic layer). Given typed data logic layer over P is a pair hQ, Ai, queries over P; (ii) A is a finite set of action Example 2. We make the scenario of Example layer L over P. L exposes two queries to inspec • Qe(e):- Emp(e) ^ ¬9t.Resp(e, t), to extract id • Qt(t, d):- Ticket(t, d), to extract tickets and t In addition, L provides three main functionalit layer: ticket registration, assignment/release, a alized through four actions (where, for simplic action and its name). The registration of a ne that, given an integer t, and two strings e and ously creates a ticket identified by t and descri assigns the employee identified by e to such tic
Example: Actions REGISTER(t,d,e): register ticket t with description d, assigning it to employee e •Add Ticket(t,d), Resp(e,t) ASSIGN(e,t): assign employee e to ticket t •Add Resp(e,t) RELEASE(e,t): release employee e from managing ticket t •Del Resp(e,t) LOG(t,e,d): flush and log the info related to ticket t •Del Ticket(t,d), Resp(e,t) •Add Log(t,e,d)
Example Idle Employees register (⌫t, emp, descr) CreateTicket Active tickets release (tid, emp) Stall assign (tid, emp) Awake Stalled tickets logData (tid, emp, id) ResolveTickets h⌫t, empi htid, empi htid, empi htid,em pi htid,em pi htid, empi hempi htid, descri htid,em pi Fig. 2. The control layer of a db-net for ticket management. In CreateTicket, ⌫t is a fresh input variable, and descr is an arbitrary input variable. Example 3. Figure 2 shows the control layer of a db-net B, using the persistence layer P defined in Example 1 and the data logic layer L defined in Example 2.2. The control layer realizes a simple ticket processing workflow, where tickets are created, Idle Employees register (⌫t, emp, descr) CreateTicket Active tickets release (tid, emp) Stall assign (tid, emp) Awake Stalled tickets logData (tid, emp, id) ResolveTickets h⌫t, empi htid, empi htid, empi htid,em pi htid,em pi htid, empi hempi htid, descri htid,em pi Fig. 2. The control layer of a db-net for ticket management. In CreateTicket, ⌫t is a fresh input variable, and descr is an arbitrary input variable. Example 3. Figure 2 shows the control layer of a db-net B, using the persistence layer P defined in Example 1 and the data logic layer L defined in Example 2.2. The control layer realizes a simple ticket processing workflow, where tickets are created, Idle Employees register (⌫t, emp, descr) CreateTicket Active tickets release (tid, emp) Stall assign (tid, emp) Awake Stalled tickets logData (tid, emp, id) ResolveTickets h⌫t, empi htid, empi htid, empi htid,em pi htid,em pi htid, empi hempi htid, descri htid,em pi Fig. 2. The control layer of a db-net for ticket management. In CreateTicket, ⌫t is a fresh input variable, and descr is an arbitrary input variable. Example 3. Figure 2 shows the control layer of a db-net B, using the persistence layer P defined in Example 1 and the data logic layer L defined in Example 2.2. The control layer realizes a simple ticket processing workflow, where tickets are created, Idle Employees register (⌫t, emp, descr) CreateTicket Active tickets release (tid, emp) Stall assign (tid, emp) Awake Stalled tickets logData (tid, emp, id) ResolveTickets h⌫t, empi htid, empi htid, empi htid,em pi htid,em pi htid, empi hempi htid, descri htid,em pi Fig. 2. The control layer of a db-net for ticket management. In CreateTicket, ⌫t is a fresh input variable, and descr is an arbitrary input variable. Example 3. Figure 2 shows the control layer of a db-net B, using the persistence layer P defined in Example 1 and the data logic layer L defined in Example 2.2. The control layer realizes a simple ticket processing workflow, where tickets are created, Idle Employees register (⌫t, emp, descr) CreateTicket Active tickets release (tid, emp) Stall assign (tid, emp) Awake Stalled tickets logData (tid, emp, id) ResolveTickets h⌫t, empi htid, empi htid, empi htid,em pi htid,em pi htid, empi hempi htid, descri htid,em pi Fig. 2. The control layer of a db-net for ticket management. In CreateTicket, ⌫t is a fresh input variable, and descr is an arbitrary input variable. Example 3. Figure 2 shows the control layer of a db-net B, using the persistence layer P defined in Example 1 and the data logic layer L defined in Example 2.2. The control layer realizes a simple ticket processing workflow, where tickets are created, Idle Employees register (⌫t, emp, descr) CreateTicket Active tickets release (tid, emp) Stall assign (tid, emp) Awake Stalled tickets logData (tid, emp, id) ResolveTickets h⌫t, empi htid, empi htid, empi htid,em pi htid,em pi htid, empi hempi htid, descri htid,em pi Fig. 2. The control layer of a db-net for ticket management. In CreateTicket, ⌫t is a fresh input variable, and descr is an arbitrary input variable. Example 3. Figure 2 shows the control layer of a db-net B, using the persistence layer P defined in Example 1 and the data logic layer L defined in Example 2.2. The control layer realizes a simple ticket processing workflow, where tickets are created, Idle Employees register (⌫t, emp, descr) CreateTicket Active tickets logData (tid, emp, id) ResolveTickets h⌫t, empi htid, emp htid,em p htid, empi hempi htid, descri Fig. 2. The control layer of a db-net for ticket manage fresh input variable, and descr is an arbitrary input var Example 3. Figure 2 shows the control layer of a db layer P defined in Example 1 and the data logic layer L control layer realizes a simple ticket processing workfl Idle Employees (⌫t, C Tickets hempi hti Fig. 2. The control fresh input variable, Example 3. Figure layer P defined in E control layer realize int int ⇥ string int ⇥ string ↵✓ can be successfully applied to I if apply(↵✓, I) complies The application of an action instance amounts to ground all in the definition of the action as specified by the given su plying the update on the given database instance, giving p over deletions (this is a standard approach, which unambi situation in which the same fact is asserted to be added and The data logic simply exposes a set of queries and a set be used by the control layer to obtain data from the persi induce updates on the persistence layer. Definition 14 (Data logic layer). Given a D-typed persi typed data logic layer over P is a pair hQ, Ai, where: (i) Q is queries over P; (ii) A is a finite set of actions over P. Example 2. We make the scenario of Example 1 operational, in layer L over P. L exposes two queries to inspect the persistence • Qe(e):- Emp(e) ^ ¬9t.Resp(e, t), to extract idle employees;Qt(t, d):- Ticket(t, d), to extract tickets and their description. n addition, L provides three main functionalities to manipulate tickets in persistence ayer: ticket registration, assignment/release, and logging. Such functionalities are re- lized through four actions (where, for simplicity, we blur the distinction between an ction and its name). The registration of a new ticket is managed by an action reg hat, given an integer t, and two strings e and d, (reg·params = ht, e, di, simultane- Case variables: - ticket id - name of responsible employee int ⇥ string
Example Idle Employees register (⌫t, emp, descr) CreateTicket Active tickets release (tid, emp) Stall assign (tid, emp) Awake Stalled tickets logData (tid, emp, id) ResolveTickets h⌫t, empi htid, empi htid, empi htid,em pi htid,em pi htid, empi hempi htid, descri htid,em pi Fig. 2. The control layer of a db-net for ticket management. In CreateTicket, ⌫t is a fresh input variable, and descr is an arbitrary input variable. Example 3. Figure 2 shows the control layer of a db-net B, using the persistence layer P defined in Example 1 and the data logic layer L defined in Example 2.2. The control layer realizes a simple ticket processing workflow, where tickets are created, Idle Employees register (⌫t, emp, descr) CreateTicket Active tickets logData (tid, emp, id) ResolveTickets h⌫t, empi htid, emp htid,em p htid, empi hempi htid, descri Fig. 2. The control layer of a db-net for ticket manage fresh input variable, and descr is an arbitrary input var Example 3. Figure 2 shows the control layer of a db layer P defined in Example 1 and the data logic layer L control layer realizes a simple ticket processing workfl Idle Employees register (⌫t, emp, descr) CreateTicket Active tickets release (tid, emp) Stall assign (tid, emp) Awake Stalled tickets logData (tid, emp, id) ResolveTickets h⌫t, empi htid, empi htid, empi htid,em pi htid,em pi htid, empi hempi htid, descri htid,em pi Fig. 2. The control layer of a db-net for ticket management. In CreateTicket, ⌫t is a fresh input variable, and descr is an arbitrary input variable. Example 3. Figure 2 shows the control layer of a db-net B, using the persistence layer P defined in Example 1 and the data logic layer L defined in Example 2.2. The control layer realizes a simple ticket processing workflow, where tickets are created, Idle Employees register (⌫t, emp, descr) CreateTicket Active tickets release (tid, emp) Stall assign (tid, emp) Awake Stalled tickets logData (tid, emp, id) ResolveTickets h⌫t, empi htid, empi htid, empi htid,em pi htid,em pi htid, empi hempi htid, descri htid,em pi Fig. 2. The control layer of a db-net for ticket management. In CreateTicket, ⌫t is a fresh input variable, and descr is an arbitrary input variable. Example 3. Figure 2 shows the control layer of a db-net B, using the persistence layer P defined in Example 1 and the data logic layer L defined in Example 2.2. The control layer realizes a simple ticket processing workflow, where tickets are created, Idle Employees register (⌫t, emp, descr) CreateTicket Active tickets release (tid, emp) Stall assign (tid, emp) Awake Stalled tickets logData (tid, emp, id) ResolveTickets h⌫t, empi htid, empi htid, empi htid,em pi htid,em pi htid, empi hempi htid, descri htid,em pi Fig. 2. The control layer of a db-net for ticket management. In CreateTicket, ⌫t is a fresh input variable, and descr is an arbitrary input variable. Example 3. Figure 2 shows the control layer of a db-net B, using the persistence layer P defined in Example 1 and the data logic layer L defined in Example 2.2. The control layer realizes a simple ticket processing workflow, where tickets are created, Idle Employees (⌫t, C Tickets hempi hti Fig. 2. The control fresh input variable, Example 3. Figure layer P defined in E control layer realize
Example Idle Employees register (⌫t, emp, descr) CreateTicket Active tickets release (tid, emp) Stall assign (tid, emp) Awake Stalled tickets logData (tid, emp, id) ResolveTickets h⌫t, empi htid, empi htid, empi htid,em pi htid,em pi htid, empi hempi htid, descri htid,em pi Fig. 2. The control layer of a db-net for ticket management. In CreateTicket, ⌫t is a fresh input variable, and descr is an arbitrary input variable. Example 3. Figure 2 shows the control layer of a db-net B, using the persistence layer P defined in Example 1 and the data logic layer L defined in Example 2.2. The control layer realizes a simple ticket processing workflow, where tickets are created, Idle Employees register (⌫t, emp, descr) CreateTicket Active tickets logData (tid, emp, id) ResolveTickets h⌫t, empi htid, emp htid,em p htid, empi hempi htid, descri Fig. 2. The control layer of a db-net for ticket manage fresh input variable, and descr is an arbitrary input var Example 3. Figure 2 shows the control layer of a db layer P defined in Example 1 and the data logic layer L control layer realizes a simple ticket processing workfl Idle Employees register (⌫t, emp, descr) CreateTicket Active tickets release (tid, emp) Stall assign (tid, emp) Awake Stalled tickets logData (tid, emp, id) ResolveTickets h⌫t, empi htid, empi htid, empi htid,em pi htid,em pi htid, empi hempi htid, descri htid,em pi Fig. 2. The control layer of a db-net for ticket management. In CreateTicket, ⌫t is a fresh input variable, and descr is an arbitrary input variable.
Example Idle Employees register (⌫t, emp, descr) CreateTicket Active tickets release (tid, emp) Stall assign (tid, emp) Awake Stalled tickets logData (tid, emp, id) ResolveTickets h⌫t, empi htid, empi htid, empi htid,em pi htid,em pi htid, empi hempi htid, descri htid,em pi Fig. 2. The control layer of a db-net for ticket management. In CreateTicket, ⌫t is a fresh input variable, and descr is an arbitrary input variable. Example 3. Figure 2 shows the control layer of a db-net B, using the persistence layer P defined in Example 1 and the data logic layer L defined in Example 2.2. The control layer realizes a simple ticket processing workflow, where tickets are created, Idle Employees register (⌫t, emp, descr) CreateTicket Active tickets logData (tid, emp, id) ResolveTickets h⌫t, empi htid, emp htid,em p htid, empi hempi htid, descri Fig. 2. The control layer of a db-net for ticket manage fresh input variable, and descr is an arbitrary input var Example 3. Figure 2 shows the control layer of a db layer P defined in Example 1 and the data logic layer L control layer realizes a simple ticket processing workfl Idle Employees register (⌫t, emp, descr) CreateTicket Active tickets release (tid, emp) Stall assign (tid, emp) Awake Stalled tickets logData (tid, emp, id) ResolveTickets h⌫t, empi htid, empi htid, empi htid,em pi htid,em pi htid, empi hempi htid, descri htid,em pi Fig. 2. The control layer of a db-net for ticket management. In CreateTicket, ⌫t is a fresh input variable, and descr is an arbitrary input variable.
Run! Idle Employees register (⌫t, emp, descr) CreateTicket Active tickets release (tid, emp) Stall assign (tid, emp) Awake Stalled tickets logData (tid, emp, id) ResolveTickets h⌫t, empi htid, empi htid, empi htid,em pi htid,em pi htid, empi hempi htid, descri htid,em pi Fig. 2. The control layer of a db-net for ticket management. In CreateTicket, ⌫t is a fresh input variable, and descr is an arbitrary input variable. Example 3. Figure 2 shows the control layer of a db-net B, using the persistence layer P defined in Example 1 and the data logic layer L defined in Example 2.2. The control layer realizes a simple ticket processing workflow, where tickets are created, Idle Employees register (⌫t, emp, descr) CreateTicket Active tickets logData (tid, emp, id) ResolveTickets h⌫t, empi htid, emp htid,em p htid, empi hempi htid, descri Fig. 2. The control layer of a db-net for ticket manage fresh input variable, and descr is an arbitrary input var Example 3. Figure 2 shows the control layer of a db layer P defined in Example 1 and the data logic layer L control layer realizes a simple ticket processing workfl Idle Employees register (⌫t, emp, descr) CreateTicket Active tickets release (tid, emp) Stall assign (tid, emp) Awake Stalled tickets logData (tid, emp, id) ResolveTickets h⌫t, empi htid, empi htid, empi htid,em pi htid,em pi htid, empi hempi htid, descri htid,em pi Fig. 2. The control layer of a db-net for ticket management. In CreateTicket, ⌫t is a fresh input variable, and descr is an arbitrary input variable. Emp Andy Marco TicketResp Log hAndyi hMarcoi
Run! Idle Employees register (⌫t, emp, descr) CreateTicket Active tickets release (tid, emp) Stall assign (tid, emp) Awake Stalled tickets logData (tid, emp, id) ResolveTickets h⌫t, empi htid, empi htid, empi htid,em pi htid,em pi htid, empi hempi htid, descri htid,em pi Fig. 2. The control layer of a db-net for ticket management. In CreateTicket, ⌫t is a fresh input variable, and descr is an arbitrary input variable. Example 3. Figure 2 shows the control layer of a db-net B, using the persistence layer P defined in Example 1 and the data logic layer L defined in Example 2.2. The control layer realizes a simple ticket processing workflow, where tickets are created, Idle Employees register (⌫t, emp, descr) CreateTicket Active tickets logData (tid, emp, id) ResolveTickets h⌫t, empi htid, emp htid,em p htid, empi hempi htid, descri Fig. 2. The control layer of a db-net for ticket manage fresh input variable, and descr is an arbitrary input var Example 3. Figure 2 shows the control layer of a db layer P defined in Example 1 and the data logic layer L control layer realizes a simple ticket processing workfl Idle Employees register (⌫t, emp, descr) CreateTicket Active tickets release (tid, emp) Stall assign (tid, emp) Awake Stalled tickets logData (tid, emp, id) ResolveTickets h⌫t, empi htid, empi htid, empi htid,em pi htid,em pi htid, empi hempi htid, descri htid,em pi Fig. 2. The control layer of a db-net for ticket management. In CreateTicket, ⌫t is a fresh input variable, and descr is an arbitrary input variable. TicketResp Log hAndyi hMarcoi ⌫t = 1 emp = Andy descr = blah Emp Andy Marco
Run! Idle Employees register (⌫t, emp, descr) CreateTicket Active tickets release (tid, emp) Stall assign (tid, emp) Awake Stalled tickets logData (tid, emp, id) ResolveTickets h⌫t, empi htid, empi htid, empi htid,em pi htid,em pi htid, empi hempi htid, descri htid,em pi Fig. 2. The control layer of a db-net for ticket management. In CreateTicket, ⌫t is a fresh input variable, and descr is an arbitrary input variable. Example 3. Figure 2 shows the control layer of a db-net B, using the persistence layer P defined in Example 1 and the data logic layer L defined in Example 2.2. The control layer realizes a simple ticket processing workflow, where tickets are created, Idle Employees register (⌫t, emp, descr) CreateTicket Active tickets logData (tid, emp, id) ResolveTickets h⌫t, empi htid, emp htid,em p htid, empi hempi htid, descri Fig. 2. The control layer of a db-net for ticket manage fresh input variable, and descr is an arbitrary input var Example 3. Figure 2 shows the control layer of a db layer P defined in Example 1 and the data logic layer L control layer realizes a simple ticket processing workfl Idle Employees register (⌫t, emp, descr) CreateTicket Active tickets release (tid, emp) Stall assign (tid, emp) Awake Stalled tickets logData (tid, emp, id) ResolveTickets h⌫t, empi htid, empi htid, empi htid,em pi htid,em pi htid, empi hempi htid, descri htid,em pi Fig. 2. The control layer of a db-net for ticket management. In CreateTicket, ⌫t is a fresh input variable, and descr is an arbitrary input variable. Ticket 1 blah Resp Andy 1 Log hMarcoi Emp Andy Marco h1, Andyi
Run! Idle Employees register (⌫t, emp, descr) CreateTicket Active tickets release (tid, emp) Stall assign (tid, emp) Awake Stalled tickets logData (tid, emp, id) ResolveTickets h⌫t, empi htid, empi htid, empi htid,em pi htid,em pi htid, empi hempi htid, descri htid,em pi Fig. 2. The control layer of a db-net for ticket management. In CreateTicket, ⌫t is a fresh input variable, and descr is an arbitrary input variable. Example 3. Figure 2 shows the control layer of a db-net B, using the persistence layer P defined in Example 1 and the data logic layer L defined in Example 2.2. The control layer realizes a simple ticket processing workflow, where tickets are created, Idle Employees register (⌫t, emp, descr) CreateTicket Active tickets logData (tid, emp, id) ResolveTickets h⌫t, empi htid, emp htid,em p htid, empi hempi htid, descri Fig. 2. The control layer of a db-net for ticket manage fresh input variable, and descr is an arbitrary input var Example 3. Figure 2 shows the control layer of a db layer P defined in Example 1 and the data logic layer L control layer realizes a simple ticket processing workfl Idle Employees register (⌫t, emp, descr) CreateTicket Active tickets release (tid, emp) Stall assign (tid, emp) Awake Stalled tickets logData (tid, emp, id) ResolveTickets h⌫t, empi htid, empi htid, empi htid,em pi htid,em pi htid, empi hempi htid, descri htid,em pi Fig. 2. The control layer of a db-net for ticket management. In CreateTicket, ⌫t is a fresh input variable, and descr is an arbitrary input variable. Ticket 1 blah Resp Andy 1 Log hMarcoi Emp Andy Marco h1, Andyi tid = 1 emp = Andy
Run! Idle Employees register (⌫t, emp, descr) CreateTicket Active tickets release (tid, emp) Stall assign (tid, emp) Awake Stalled tickets logData (tid, emp, id) ResolveTickets h⌫t, empi htid, empi htid, empi htid,em pi htid,em pi htid, empi hempi htid, descri htid,em pi Fig. 2. The control layer of a db-net for ticket management. In CreateTicket, ⌫t is a fresh input variable, and descr is an arbitrary input variable. Example 3. Figure 2 shows the control layer of a db-net B, using the persistence layer P defined in Example 1 and the data logic layer L defined in Example 2.2. The control layer realizes a simple ticket processing workflow, where tickets are created, Idle Employees register (⌫t, emp, descr) CreateTicket Active tickets logData (tid, emp, id) ResolveTickets h⌫t, empi htid, emp htid,em p htid, empi hempi htid, descri Fig. 2. The control layer of a db-net for ticket manage fresh input variable, and descr is an arbitrary input var Example 3. Figure 2 shows the control layer of a db layer P defined in Example 1 and the data logic layer L control layer realizes a simple ticket processing workfl Idle Employees register (⌫t, emp, descr) CreateTicket Active tickets release (tid, emp) Stall assign (tid, emp) Awake Stalled tickets logData (tid, emp, id) ResolveTickets h⌫t, empi htid, empi htid, empi htid,em pi htid,em pi htid, empi hempi htid, descri htid,em pi Fig. 2. The control layer of a db-net for ticket management. In CreateTicket, ⌫t is a fresh input variable, and descr is an arbitrary input variable. Resp Log hAndyi hMarcoi Emp Andy Marco Ticket 1 blah h1, blahi h1, Andyi
DB-nets Enactment CPN Tools extension (works also for the previous PN models we have seen)
Verification of DB-nets Encoding of DB-nets into DCDSs Bounded DB-nets translate into state-bounded DCDSs <— Verification against FO temporal logics, limited to string
 and real domains without arithmetics —> Unfortunately, CPN Tools does not allow to extend the
 state space construction mechanism • We have provided an encoding from DB-nets to CPNs, restricting the query language to UCQs with filters
• Explicit control-flow • Local data (case perspective) • Global data (persistent storage) • Updatable global data • External inputs • Support of automated analysis Cooking with DB-Nets FINALLY!
Conclusion A cookbook for integrated models of data and processes • with increasing sophistication of the data component • paying attention to modeling, enactment, and automated analysis Which models for which scenarios? • Application of DB-nets to application integration patterns [EDOC 2018] Some open points for modeling • Arithmetics? • Connection with front-end languages 
 (data-aware BPMN soon to come) • Connection with interaction models (Proclets)
Parameterized Verification Split the persistent storage into a read-only database, and a read- write working memory Verification “irrespectively” of the content of the read-only DB • Extensively studied in database theory (Deutsch, Hull, Vianu, …) • by carefully limiting the expressive power of all components (fragile setting) • by considering arithmetic in the picture • Recently attacked by us using SMT model checking for array- based systems
Declarative Data-Aware Processes: the OCBC Approach 10 A. Artale, D. Calvanese, M. Montali, and W. van der Aalst is about 1 1 creates 1 promotes 1 creates 1 1 stops 1 closes 1 Person Candidate Application Job Offer Job Profile1 / made by 1..⇤ ⇤ responds to 1 ⇤ refers to 1 register data submit mark as eligible post offer cancel hiring determine winner Fig. 5: Activity-object relationships in the job hiring scenario of Section 2.1
Declarative Data-Aware Processes: the OCBC Approach 10 A. Artale, D. Calvanese, M. Montali, and W. van der Aalst is about 1 1 creates 1 promotes 1 creates 1 1 stops 1 closes 1 Person Candidate Application Job Offer Job Profile1 / made by 1..⇤ ⇤ responds to 1 ⇤ refers to 1 register data submit mark as eligible post offer cancel hiring determine winner Fig. 5: Activity-object relationships in the job hiring scenario of Section 2.1
Declarative Data-Aware Processes: the OCBC Approach Enriching Data Models with Behavioral Constraints 13 is about 1 1 creates 1 promotes 1 creates 1 1 stops 1 closes 1 Person Candidate Application Job Offer Job Profile1 / made by 1..⇤ ⇤ responds to 1 ⇤ refers to 1 register data submit mark as eligible post offer cancel hiring determine winner Fig. 7: OCBC model for the job hiring scenario of Section 2.1, where each one of C.1–C.11 therein corresponds to either an activity-object relationship or a co-reference temporal constraint in the OCBC model. The lightweight constraint is redundant: it is implied by the other constraints in the diagram. Formal semantics with temporal description logics (Satisfiability EXPTIME-complete)
EXAMPLES
ExampleProcesses and Data The process model The data model Modeling and In-Database Management of Relational, Data-Aware Processes start Start Workflow Review Request Fill Reimb. accepted Review Reimb. End Workflow rejected end Pending ID: int empl : string dest : string Accepted ID :int empl : string dest : string cost : int Rejected ID : int empl : string dest : string TrvlMaxAmnt ID : int FID : int maxAmnt : int CurrReq ID: int empl : string dest : string status : string {submitd, acceptd, reimbd, rejd, complete} TrvlCost ID : int FID : int cost : int FK_TrvlMaxAmnt_CurrReq FK_TrvlCost_CurrReq start workflow submitted request review request <ν,e,d,“submitted”> <id,e,d,status><e,d>
ExampleProcesses and Data The process model The data model Modeling and In-Database Management of Relational, Data-Aware Processes start Start Workflow Review Request Fill Reimb. accepted Review Reimb. End Workflow rejected end Pending ID: int empl : string dest : string Accepted ID :int empl : string dest : string cost : int Rejected ID : int empl : string dest : string TrvlMaxAmnt ID : int FID : int maxAmnt : int CurrReq ID: int empl : string dest : string status : string {submitd, acceptd, reimbd, rejd, complete} TrvlCost ID : int FID : int cost : int FK_TrvlMaxAmnt_CurrReq FK_TrvlCost_CurrReq start workflow submitted request review request <ν,e,d,“submitted”> <id,e,d,status>
Persistence Layer Typed relational DB with constraints • DB: set of relation schemas with typed components • Type: data domain with rigidly defined predicates • Constraints: Domain-independent FO sentences • Keys, FKs, dependencies, multiplicities, … DB Instance: finite set of typed facts over DB, satisfying all constraints
Example Emp name: string Ticket id: int descr: string Resp emp: string ticket: int Each employee can handle at most one ticket at a given time Log ticket: int emp: string descr: string
Example: Queries • Get tickets and their description • Get “idle” employees plying the update on the given database in over deletions (this is a standard approach situation in which the same fact is asserted The data logic simply exposes a set of q be used by the control layer to obtain data induce updates on the persistence layer. Definition 14 (Data logic layer). Given typed data logic layer over P is a pair hQ, Ai, queries over P; (ii) A is a finite set of action Example 2. We make the scenario of Example layer L over P. L exposes two queries to inspec • Qe(e):- Emp(e) ^ ¬9t.Resp(e, t), to extract id • Qt(t, d):- Ticket(t, d), to extract tickets and t In addition, L provides three main functionalit layer: ticket registration, assignment/release, a alized through four actions (where, for simplic action and its name). The registration of a ne that, given an integer t, and two strings e and ously creates a ticket identified by t and descri assigns the employee identified by e to such tic
Example: Actions REGISTER(t,d,e): register ticket t with description d, assigning it to employee e •Add Ticket(t,d), Resp(e,t) ASSIGN(e,t): assign employee e to ticket t •Add Resp(e,t) RELEASE(e,t): release employee e from managing ticket t •Del Resp(e,t) LOG(t,e,d): flush and log the info related to ticket t •Del Ticket(t,d), Resp(e,t) •Add Log(t,e,d)
Control Layer A “data-oriented” CPN • Process control-flow • Evolution of tokens and their “case” data • Interaction with the persistence layer via the data logic layer
Normal Place • Represent case states and resources • Color: schema of the local data carried by tokens • May be seen as a special relation of the persistence layer • Tokens explicitly manipulated by the control layer, as customary in CPNs Tickets h Fig. 2. Th
View Place • “View” of the persistence layer provided to the control layer • Hosts the answers to a query from the data logic • Clearly identifies where the control layer needs to “read” from the persistence layer • Not modified explicitly by the control layer • Implicitly updated by applying actions on the persistence layer, and recomputing the view Tickets h Fig. 2. Th
Example Idle Employees register (⌫t, emp, descr) CreateTicket Active tickets release (tid, emp) Stall assign (tid, emp) Awake Stalled tickets logData (tid, emp, id) ResolveTickets h⌫t, empi htid, empi htid, empi htid,em pi htid,em pi htid, empi hempi htid, descri htid,em pi Fig. 2. The control layer of a db-net for ticket management. In CreateTicket, ⌫t is a fresh input variable, and descr is an arbitrary input variable. Example 3. Figure 2 shows the control layer of a db-net B, using the persistence layer P defined in Example 1 and the data logic layer L defined in Example 2.2. The control layer realizes a simple ticket processing workflow, where tickets are created, Idle Employees register (⌫t, emp, descr) CreateTicket Active tickets release (tid, emp) Stall assign (tid, emp) Awake Stalled tickets logData (tid, emp, id) ResolveTickets h⌫t, empi htid, empi htid, empi htid,em pi htid,em pi htid, empi hempi htid, descri htid,em pi Fig. 2. The control layer of a db-net for ticket management. In CreateTicket, ⌫t is a fresh input variable, and descr is an arbitrary input variable. Example 3. Figure 2 shows the control layer of a db-net B, using the persistence layer P defined in Example 1 and the data logic layer L defined in Example 2.2. The control layer realizes a simple ticket processing workflow, where tickets are created, Idle Employees register (⌫t, emp, descr) CreateTicket Active tickets release (tid, emp) Stall assign (tid, emp) Awake Stalled tickets logData (tid, emp, id) ResolveTickets h⌫t, empi htid, empi htid, empi htid,em pi htid,em pi htid, empi hempi htid, descri htid,em pi Fig. 2. The control layer of a db-net for ticket management. In CreateTicket, ⌫t is a fresh input variable, and descr is an arbitrary input variable. Example 3. Figure 2 shows the control layer of a db-net B, using the persistence layer P defined in Example 1 and the data logic layer L defined in Example 2.2. The control layer realizes a simple ticket processing workflow, where tickets are created, Idle Employees register (⌫t, emp, descr) CreateTicket Active tickets release (tid, emp) Stall assign (tid, emp) Awake Stalled tickets logData (tid, emp, id) ResolveTickets h⌫t, empi htid, empi htid, empi htid,em pi htid,em pi htid, empi hempi htid, descri htid,em pi Fig. 2. The control layer of a db-net for ticket management. In CreateTicket, ⌫t is a fresh input variable, and descr is an arbitrary input variable. Example 3. Figure 2 shows the control layer of a db-net B, using the persistence layer P defined in Example 1 and the data logic layer L defined in Example 2.2. The control layer realizes a simple ticket processing workflow, where tickets are created, Idle Employees register (⌫t, emp, descr) CreateTicket Active tickets release (tid, emp) Stall assign (tid, emp) Awake Stalled tickets logData (tid, emp, id) ResolveTickets h⌫t, empi htid, empi htid, empi htid,em pi htid,em pi htid, empi hempi htid, descri htid,em pi Fig. 2. The control layer of a db-net for ticket management. In CreateTicket, ⌫t is a fresh input variable, and descr is an arbitrary input variable. Example 3. Figure 2 shows the control layer of a db-net B, using the persistence layer P defined in Example 1 and the data logic layer L defined in Example 2.2. The control layer realizes a simple ticket processing workflow, where tickets are created, Idle Employees register (⌫t, emp, descr) CreateTicket Active tickets release (tid, emp) Stall assign (tid, emp) Awake Stalled tickets logData (tid, emp, id) ResolveTickets h⌫t, empi htid, empi htid, empi htid,em pi htid,em pi htid, empi hempi htid, descri htid,em pi Fig. 2. The control layer of a db-net for ticket management. In CreateTicket, ⌫t is a fresh input variable, and descr is an arbitrary input variable. Example 3. Figure 2 shows the control layer of a db-net B, using the persistence layer P defined in Example 1 and the data logic layer L defined in Example 2.2. The control layer realizes a simple ticket processing workflow, where tickets are created, Idle Employees register (⌫t, emp, descr) CreateTicket Active tickets logData (tid, emp, id) ResolveTickets h⌫t, empi htid, emp htid,em p htid, empi hempi htid, descri Fig. 2. The control layer of a db-net for ticket manage fresh input variable, and descr is an arbitrary input var Example 3. Figure 2 shows the control layer of a db layer P defined in Example 1 and the data logic layer L control layer realizes a simple ticket processing workfl Idle Employees (⌫t, C Tickets hempi hti Fig. 2. The control fresh input variable, Example 3. Figure layer P defined in E control layer realize int int ⇥ string int ⇥ string ↵✓ can be successfully applied to I if apply(↵✓, I) complies The application of an action instance amounts to ground all in the definition of the action as specified by the given su plying the update on the given database instance, giving p over deletions (this is a standard approach, which unambi situation in which the same fact is asserted to be added and The data logic simply exposes a set of queries and a set be used by the control layer to obtain data from the persi induce updates on the persistence layer. Definition 14 (Data logic layer). Given a D-typed persi typed data logic layer over P is a pair hQ, Ai, where: (i) Q is queries over P; (ii) A is a finite set of actions over P. Example 2. We make the scenario of Example 1 operational, in layer L over P. L exposes two queries to inspect the persistence • Qe(e):- Emp(e) ^ ¬9t.Resp(e, t), to extract idle employees;Qt(t, d):- Ticket(t, d), to extract tickets and their description. n addition, L provides three main functionalities to manipulate tickets in persistence ayer: ticket registration, assignment/release, and logging. Such functionalities are re- lized through four actions (where, for simplicity, we blur the distinction between an ction and its name). The registration of a new ticket is managed by an action reg hat, given an integer t, and two strings e and d, (reg·params = ht, e, di, simultane- Case variables: - ticket id - name of responsible employee int ⇥ string
Transition Atomic unit of work within the control layer • Input data: obtained by • Consuming tokens from its input places • Reading tokens from its input view places • To access tokens and their data: multisets of tuples of “matching” variables • Data guard over the input variables
Transition Atomic unit of work within the control layer • Output data: inputs + additional variables (external input) + ν variables (new ids) • Output data used to • Bind to an action of the data logic, updating the persistence layer • Produce tokens and insert them into the output places
Example Idle Employees register (⌫t, emp, descr) CreateTicket Active tickets release (tid, emp) Stall assign (tid, emp) Awake Stalled tickets logData (tid, emp, id) ResolveTickets h⌫t, empi htid, empi htid, empi htid,em pi htid,em pi htid, empi hempi htid, descri htid,em pi Fig. 2. The control layer of a db-net for ticket management. In CreateTicket, ⌫t is a fresh input variable, and descr is an arbitrary input variable. Example 3. Figure 2 shows the control layer of a db-net B, using the persistence layer P defined in Example 1 and the data logic layer L defined in Example 2.2. The control layer realizes a simple ticket processing workflow, where tickets are created, Idle Employees register (⌫t, emp, descr) CreateTicket Active tickets logData (tid, emp, id) ResolveTickets h⌫t, empi htid, emp htid,em p htid, empi hempi htid, descri Fig. 2. The control layer of a db-net for ticket manage fresh input variable, and descr is an arbitrary input var Example 3. Figure 2 shows the control layer of a db layer P defined in Example 1 and the data logic layer L control layer realizes a simple ticket processing workfl Idle Employees register (⌫t, emp, descr) CreateTicket Active tickets release (tid, emp) Stall assign (tid, emp) Awake Stalled tickets logData (tid, emp, id) ResolveTickets h⌫t, empi htid, empi htid, empi htid,em pi htid,em pi htid, empi hempi htid, descri htid,em pi Fig. 2. The control layer of a db-net for ticket management. In CreateTicket, ⌫t is a fresh input variable, and descr is an arbitrary input variable. Example 3. Figure 2 shows the control layer of a db-net B, using the persistence layer P defined in Example 1 and the data logic layer L defined in Example 2.2. The control layer realizes a simple ticket processing workflow, where tickets are created, Idle Employees register (⌫t, emp, descr) CreateTicket Active tickets release (tid, emp) Stall assign (tid, emp) Awake Stalled tickets logData (tid, emp, id) ResolveTickets h⌫t, empi htid, empi htid, empi htid,em pi htid,em pi htid, empi hempi htid, descri htid,em pi Fig. 2. The control layer of a db-net for ticket management. In CreateTicket, ⌫t is a fresh input variable, and descr is an arbitrary input variable. Example 3. Figure 2 shows the control layer of a db-net B, using the persistence layer P defined in Example 1 and the data logic layer L defined in Example 2.2. The control layer realizes a simple ticket processing workflow, where tickets are created, Idle Employees register (⌫t, emp, descr) CreateTicket Active tickets release (tid, emp) Stall assign (tid, emp) Awake Stalled tickets logData (tid, emp, id) ResolveTickets h⌫t, empi htid, empi htid, empi htid,em pi htid,em pi htid, empi hempi htid, descri htid,em pi Fig. 2. The control layer of a db-net for ticket management. In CreateTicket, ⌫t is a fresh input variable, and descr is an arbitrary input variable. Example 3. Figure 2 shows the control layer of a db-net B, using the persistence layer P defined in Example 1 and the data logic layer L defined in Example 2.2. The control layer realizes a simple ticket processing workflow, where tickets are created, Idle Employees (⌫t, C Tickets hempi hti Fig. 2. The control fresh input variable, Example 3. Figure layer P defined in E control layer realize
Example Idle Employees register (⌫t, emp, descr) CreateTicket Active tickets release (tid, emp) Stall assign (tid, emp) Awake Stalled tickets logData (tid, emp, id) ResolveTickets h⌫t, empi htid, empi htid, empi htid,em pi htid,em pi htid, empi hempi htid, descri htid,em pi Fig. 2. The control layer of a db-net for ticket management. In CreateTicket, ⌫t is a fresh input variable, and descr is an arbitrary input variable. Example 3. Figure 2 shows the control layer of a db-net B, using the persistence layer P defined in Example 1 and the data logic layer L defined in Example 2.2. The control layer realizes a simple ticket processing workflow, where tickets are created, Idle Employees register (⌫t, emp, descr) CreateTicket Active tickets logData (tid, emp, id) ResolveTickets h⌫t, empi htid, emp htid,em p htid, empi hempi htid, descri Fig. 2. The control layer of a db-net for ticket manage fresh input variable, and descr is an arbitrary input var Example 3. Figure 2 shows the control layer of a db layer P defined in Example 1 and the data logic layer L control layer realizes a simple ticket processing workfl Idle Employees register (⌫t, emp, descr) CreateTicket Active tickets release (tid, emp) Stall assign (tid, emp) Awake Stalled tickets logData (tid, emp, id) ResolveTickets h⌫t, empi htid, empi htid, empi htid,em pi htid,em pi htid, empi hempi htid, descri htid,em pi Fig. 2. The control layer of a db-net for ticket management. In CreateTicket, ⌫t is a fresh input variable, and descr is an arbitrary input variable.
Example Idle Employees register (⌫t, emp, descr) CreateTicket Active tickets release (tid, emp) Stall assign (tid, emp) Awake Stalled tickets logData (tid, emp, id) ResolveTickets h⌫t, empi htid, empi htid, empi htid,em pi htid,em pi htid, empi hempi htid, descri htid,em pi Fig. 2. The control layer of a db-net for ticket management. In CreateTicket, ⌫t is a fresh input variable, and descr is an arbitrary input variable. Example 3. Figure 2 shows the control layer of a db-net B, using the persistence layer P defined in Example 1 and the data logic layer L defined in Example 2.2. The control layer realizes a simple ticket processing workflow, where tickets are created, Idle Employees register (⌫t, emp, descr) CreateTicket Active tickets logData (tid, emp, id) ResolveTickets h⌫t, empi htid, emp htid,em p htid, empi hempi htid, descri Fig. 2. The control layer of a db-net for ticket manage fresh input variable, and descr is an arbitrary input var Example 3. Figure 2 shows the control layer of a db layer P defined in Example 1 and the data logic layer L control layer realizes a simple ticket processing workfl Idle Employees register (⌫t, emp, descr) CreateTicket Active tickets release (tid, emp) Stall assign (tid, emp) Awake Stalled tickets logData (tid, emp, id) ResolveTickets h⌫t, empi htid, empi htid, empi htid,em pi htid,em pi htid, empi hempi htid, descri htid,em pi Fig. 2. The control layer of a db-net for ticket management. In CreateTicket, ⌫t is a fresh input variable, and descr is an arbitrary input variable.
Run! Idle Employees register (⌫t, emp, descr) CreateTicket Active tickets release (tid, emp) Stall assign (tid, emp) Awake Stalled tickets logData (tid, emp, id) ResolveTickets h⌫t, empi htid, empi htid, empi htid,em pi htid,em pi htid, empi hempi htid, descri htid,em pi Fig. 2. The control layer of a db-net for ticket management. In CreateTicket, ⌫t is a fresh input variable, and descr is an arbitrary input variable. Example 3. Figure 2 shows the control layer of a db-net B, using the persistence layer P defined in Example 1 and the data logic layer L defined in Example 2.2. The control layer realizes a simple ticket processing workflow, where tickets are created, Idle Employees register (⌫t, emp, descr) CreateTicket Active tickets logData (tid, emp, id) ResolveTickets h⌫t, empi htid, emp htid,em p htid, empi hempi htid, descri Fig. 2. The control layer of a db-net for ticket manage fresh input variable, and descr is an arbitrary input var Example 3. Figure 2 shows the control layer of a db layer P defined in Example 1 and the data logic layer L control layer realizes a simple ticket processing workfl Idle Employees register (⌫t, emp, descr) CreateTicket Active tickets release (tid, emp) Stall assign (tid, emp) Awake Stalled tickets logData (tid, emp, id) ResolveTickets h⌫t, empi htid, empi htid, empi htid,em pi htid,em pi htid, empi hempi htid, descri htid,em pi Fig. 2. The control layer of a db-net for ticket management. In CreateTicket, ⌫t is a fresh input variable, and descr is an arbitrary input variable. Emp Andy Marco TicketResp Log hAndyi hMarcoi
Run! Idle Employees register (⌫t, emp, descr) CreateTicket Active tickets release (tid, emp) Stall assign (tid, emp) Awake Stalled tickets logData (tid, emp, id) ResolveTickets h⌫t, empi htid, empi htid, empi htid,em pi htid,em pi htid, empi hempi htid, descri htid,em pi Fig. 2. The control layer of a db-net for ticket management. In CreateTicket, ⌫t is a fresh input variable, and descr is an arbitrary input variable. Example 3. Figure 2 shows the control layer of a db-net B, using the persistence layer P defined in Example 1 and the data logic layer L defined in Example 2.2. The control layer realizes a simple ticket processing workflow, where tickets are created, Idle Employees register (⌫t, emp, descr) CreateTicket Active tickets logData (tid, emp, id) ResolveTickets h⌫t, empi htid, emp htid,em p htid, empi hempi htid, descri Fig. 2. The control layer of a db-net for ticket manage fresh input variable, and descr is an arbitrary input var Example 3. Figure 2 shows the control layer of a db layer P defined in Example 1 and the data logic layer L control layer realizes a simple ticket processing workfl Idle Employees register (⌫t, emp, descr) CreateTicket Active tickets release (tid, emp) Stall assign (tid, emp) Awake Stalled tickets logData (tid, emp, id) ResolveTickets h⌫t, empi htid, empi htid, empi htid,em pi htid,em pi htid, empi hempi htid, descri htid,em pi Fig. 2. The control layer of a db-net for ticket management. In CreateTicket, ⌫t is a fresh input variable, and descr is an arbitrary input variable. TicketResp Log hAndyi hMarcoi ⌫t = 1 emp = Andy descr = blah Emp Andy Marco
Run! Idle Employees register (⌫t, emp, descr) CreateTicket Active tickets release (tid, emp) Stall assign (tid, emp) Awake Stalled tickets logData (tid, emp, id) ResolveTickets h⌫t, empi htid, empi htid, empi htid,em pi htid,em pi htid, empi hempi htid, descri htid,em pi Fig. 2. The control layer of a db-net for ticket management. In CreateTicket, ⌫t is a fresh input variable, and descr is an arbitrary input variable. Example 3. Figure 2 shows the control layer of a db-net B, using the persistence layer P defined in Example 1 and the data logic layer L defined in Example 2.2. The control layer realizes a simple ticket processing workflow, where tickets are created, Idle Employees register (⌫t, emp, descr) CreateTicket Active tickets logData (tid, emp, id) ResolveTickets h⌫t, empi htid, emp htid,em p htid, empi hempi htid, descri Fig. 2. The control layer of a db-net for ticket manage fresh input variable, and descr is an arbitrary input var Example 3. Figure 2 shows the control layer of a db layer P defined in Example 1 and the data logic layer L control layer realizes a simple ticket processing workfl Idle Employees register (⌫t, emp, descr) CreateTicket Active tickets release (tid, emp) Stall assign (tid, emp) Awake Stalled tickets logData (tid, emp, id) ResolveTickets h⌫t, empi htid, empi htid, empi htid,em pi htid,em pi htid, empi hempi htid, descri htid,em pi Fig. 2. The control layer of a db-net for ticket management. In CreateTicket, ⌫t is a fresh input variable, and descr is an arbitrary input variable. Ticket 1 blah Resp Andy 1 Log hMarcoi Emp Andy Marco h1, Andyi
Run! Idle Employees register (⌫t, emp, descr) CreateTicket Active tickets release (tid, emp) Stall assign (tid, emp) Awake Stalled tickets logData (tid, emp, id) ResolveTickets h⌫t, empi htid, empi htid, empi htid,em pi htid,em pi htid, empi hempi htid, descri htid,em pi Fig. 2. The control layer of a db-net for ticket management. In CreateTicket, ⌫t is a fresh input variable, and descr is an arbitrary input variable. Example 3. Figure 2 shows the control layer of a db-net B, using the persistence layer P defined in Example 1 and the data logic layer L defined in Example 2.2. The control layer realizes a simple ticket processing workflow, where tickets are created, Idle Employees register (⌫t, emp, descr) CreateTicket Active tickets logData (tid, emp, id) ResolveTickets h⌫t, empi htid, emp htid,em p htid, empi hempi htid, descri Fig. 2. The control layer of a db-net for ticket manage fresh input variable, and descr is an arbitrary input var Example 3. Figure 2 shows the control layer of a db layer P defined in Example 1 and the data logic layer L control layer realizes a simple ticket processing workfl Idle Employees register (⌫t, emp, descr) CreateTicket Active tickets release (tid, emp) Stall assign (tid, emp) Awake Stalled tickets logData (tid, emp, id) ResolveTickets h⌫t, empi htid, empi htid, empi htid,em pi htid,em pi htid, empi hempi htid, descri htid,em pi Fig. 2. The control layer of a db-net for ticket management. In CreateTicket, ⌫t is a fresh input variable, and descr is an arbitrary input variable. Ticket 1 blah Resp Andy 1 Log hMarcoi Emp Andy Marco h1, Andyi tid = 1 emp = Andy
Run! Idle Employees register (⌫t, emp, descr) CreateTicket Active tickets release (tid, emp) Stall assign (tid, emp) Awake Stalled tickets logData (tid, emp, id) ResolveTickets h⌫t, empi htid, empi htid, empi htid,em pi htid,em pi htid, empi hempi htid, descri htid,em pi Fig. 2. The control layer of a db-net for ticket management. In CreateTicket, ⌫t is a fresh input variable, and descr is an arbitrary input variable. Example 3. Figure 2 shows the control layer of a db-net B, using the persistence layer P defined in Example 1 and the data logic layer L defined in Example 2.2. The control layer realizes a simple ticket processing workflow, where tickets are created, Idle Employees register (⌫t, emp, descr) CreateTicket Active tickets logData (tid, emp, id) ResolveTickets h⌫t, empi htid, emp htid,em p htid, empi hempi htid, descri Fig. 2. The control layer of a db-net for ticket manage fresh input variable, and descr is an arbitrary input var Example 3. Figure 2 shows the control layer of a db layer P defined in Example 1 and the data logic layer L control layer realizes a simple ticket processing workfl Idle Employees register (⌫t, emp, descr) CreateTicket Active tickets release (tid, emp) Stall assign (tid, emp) Awake Stalled tickets logData (tid, emp, id) ResolveTickets h⌫t, empi htid, empi htid, empi htid,em pi htid,em pi htid, empi hempi htid, descri htid,em pi Fig. 2. The control layer of a db-net for ticket management. In CreateTicket, ⌫t is a fresh input variable, and descr is an arbitrary input variable. Resp Log hAndyi hMarcoi Emp Andy Marco Ticket 1 blah h1, blahi h1, Andyi
Run! Idle Employees register (⌫t, emp, descr) CreateTicket Active tickets release (tid, emp) Stall assign (tid, emp) Awake Stalled tickets logData (tid, emp, id) ResolveTickets h⌫t, empi htid, empi htid, empi htid,em pi htid,em pi htid, empi hempi htid, descri htid,em pi Fig. 2. The control layer of a db-net for ticket management. In CreateTicket, ⌫t is a fresh input variable, and descr is an arbitrary input variable. Example 3. Figure 2 shows the control layer of a db-net B, using the persistence layer P defined in Example 1 and the data logic layer L defined in Example 2.2. The control layer realizes a simple ticket processing workflow, where tickets are created, Idle Employees register (⌫t, emp, descr) CreateTicket Active tickets logData (tid, emp, id) ResolveTickets h⌫t, empi htid, emp htid,em p htid, empi hempi htid, descri Fig. 2. The control layer of a db-net for ticket manage fresh input variable, and descr is an arbitrary input var Example 3. Figure 2 shows the control layer of a db layer P defined in Example 1 and the data logic layer L control layer realizes a simple ticket processing workfl Idle Employees register (⌫t, emp, descr) CreateTicket Active tickets release (tid, emp) Stall assign (tid, emp) Awake Stalled tickets logData (tid, emp, id) ResolveTickets h⌫t, empi htid, empi htid, empi htid,em pi htid,em pi htid, empi hempi htid, descri htid,em pi Fig. 2. The control layer of a db-net for ticket management. In CreateTicket, ⌫t is a fresh input variable, and descr is an arbitrary input variable. Resp Log hAndyi hMarcoi Emp Andy Marco Ticket 1 blah h1, blahi h1, Andyi ⌫t = 5 emp = Andy descr = blah
Run! Idle Employees register (⌫t, emp, descr) CreateTicket Active tickets release (tid, emp) Stall assign (tid, emp) Awake Stalled tickets logData (tid, emp, id) ResolveTickets h⌫t, empi htid, empi htid, empi htid,em pi htid,em pi htid, empi hempi htid, descri htid,em pi Fig. 2. The control layer of a db-net for ticket management. In CreateTicket, ⌫t is a fresh input variable, and descr is an arbitrary input variable. Example 3. Figure 2 shows the control layer of a db-net B, using the persistence layer P defined in Example 1 and the data logic layer L defined in Example 2.2. The control layer realizes a simple ticket processing workflow, where tickets are created, Idle Employees register (⌫t, emp, descr) CreateTicket Active tickets logData (tid, emp, id) ResolveTickets h⌫t, empi htid, emp htid,em p htid, empi hempi htid, descri Fig. 2. The control layer of a db-net for ticket manage fresh input variable, and descr is an arbitrary input var Example 3. Figure 2 shows the control layer of a db layer P defined in Example 1 and the data logic layer L control layer realizes a simple ticket processing workfl Idle Employees register (⌫t, emp, descr) CreateTicket Active tickets release (tid, emp) Stall assign (tid, emp) Awake Stalled tickets logData (tid, emp, id) ResolveTickets h⌫t, empi htid, empi htid, empi htid,em pi htid,em pi htid, empi hempi htid, descri htid,em pi Fig. 2. The control layer of a db-net for ticket management. In CreateTicket, ⌫t is a fresh input variable, and descr is an arbitrary input variable. Log hMarcoi Emp Andy Marco Ticket 1 blah 2 blah h1, blahi h1, Andyi Resp Andy 2 h2, Andyi h2, blahi
Run! Idle Employees register (⌫t, emp, descr) CreateTicket Active tickets release (tid, emp) Stall assign (tid, emp) Awake Stalled tickets logData (tid, emp, id) ResolveTickets h⌫t, empi htid, empi htid, empi htid,em pi htid,em pi htid, empi hempi htid, descri htid,em pi Fig. 2. The control layer of a db-net for ticket management. In CreateTicket, ⌫t is a fresh input variable, and descr is an arbitrary input variable. Example 3. Figure 2 shows the control layer of a db-net B, using the persistence layer P defined in Example 1 and the data logic layer L defined in Example 2.2. The control layer realizes a simple ticket processing workflow, where tickets are created, Idle Employees register (⌫t, emp, descr) CreateTicket Active tickets logData (tid, emp, id) ResolveTickets h⌫t, empi htid, emp htid,em p htid, empi hempi htid, descri Fig. 2. The control layer of a db-net for ticket manage fresh input variable, and descr is an arbitrary input var Example 3. Figure 2 shows the control layer of a db layer P defined in Example 1 and the data logic layer L control layer realizes a simple ticket processing workfl Idle Employees register (⌫t, emp, descr) CreateTicket Active tickets release (tid, emp) Stall assign (tid, emp) Awake Stalled tickets logData (tid, emp, id) ResolveTickets h⌫t, empi htid, empi htid, empi htid,em pi htid,em pi htid, empi hempi htid, descri htid,em pi Fig. 2. The control layer of a db-net for ticket management. In CreateTicket, ⌫t is a fresh input variable, and descr is an arbitrary input variable. Log hMarcoi Emp Andy Marco Ticket 1 blah 2 blah h1, Andyi Resp Andy 2 h2, Andyi tid = 1 emp = Andy h1, blahi h2, blahi
Run? Idle Employees register (⌫t, emp, descr) CreateTicket Active tickets release (tid, emp) Stall assign (tid, emp) Awake Stalled tickets logData (tid, emp, id) ResolveTickets h⌫t, empi htid, empi htid, empi htid,em pi htid,em pi htid, empi hempi htid, descri htid,em pi Fig. 2. The control layer of a db-net for ticket management. In CreateTicket, ⌫t is a fresh input variable, and descr is an arbitrary input variable. Example 3. Figure 2 shows the control layer of a db-net B, using the persistence layer P defined in Example 1 and the data logic layer L defined in Example 2.2. The control layer realizes a simple ticket processing workflow, where tickets are created, Idle Employees register (⌫t, emp, descr) CreateTicket Active tickets logData (tid, emp, id) ResolveTickets h⌫t, empi htid, emp htid,em p htid, empi hempi htid, descri Fig. 2. The control layer of a db-net for ticket manage fresh input variable, and descr is an arbitrary input var Example 3. Figure 2 shows the control layer of a db layer P defined in Example 1 and the data logic layer L control layer realizes a simple ticket processing workfl Idle Employees register (⌫t, emp, descr) CreateTicket Active tickets release (tid, emp) Stall assign (tid, emp) Awake Stalled tickets logData (tid, emp, id) ResolveTickets h⌫t, empi htid, empi htid, empi htid,em pi htid,em pi htid, empi hempi htid, descri htid,em pi Fig. 2. The control layer of a db-net for ticket management. In CreateTicket, ⌫t is a fresh input variable, and descr is an arbitrary input variable. Log hMarcoi Emp Andy Marco Ticket 1 blah 2 blah h1, Andyi Resp Andy 2 Andy 1 h2, Andyi tid = 1 emp = Andy h1, blahi h2, blahi
Rollback Flow Accounts for the production and routing of tokens when the application of a ground action fails • update ok: update committed on the DB, normal output flow used, rollback flow ignored • update violates some constraint: rollback on the DB, rollback flow used, normal output flow ignored The rollback flow can be used to model “undo” or “compensation” in the control layer when the persistence layer rejects an update
Example: “Undo” Idle Employees register (⌫t, emp, descr) CreateTicket Active tickets release (tid, emp) Stall assign (tid, emp) Awake Stalled tickets logData (tid, emp, id) ResolveTickets h⌫t, empi htid, empi htid, empi htid,em pi htid,em pi htid, empi hempi htid, descri htid,em pi Fig. 2. The control layer of a db-net for ticket management. In CreateTicket, ⌫t is a fresh input variable, and descr is an arbitrary input variable. Example 3. Figure 2 shows the control layer of a db-net B, using the persistence layer P defined in Example 1 and the data logic layer L defined in Example 2.2. The control layer realizes a simple ticket processing workflow, where tickets are created,
Run! Idle Employees register (⌫t, emp, descr) CreateTicket Active tickets release (tid, emp) Stall assign (tid, emp) Awake Stalled tickets logData (tid, emp, id) ResolveTickets h⌫t, empi htid, empi htid, empi htid,em pi htid,em pi htid, empi hempi htid, descri htid,em pi Fig. 2. The control layer of a db-net for ticket management. In CreateTicket, ⌫t is a fresh input variable, and descr is an arbitrary input variable. Example 3. Figure 2 shows the control layer of a db-net B, using the persistence layer P defined in Example 1 and the data logic layer L defined in Example 2.2. The control layer realizes a simple ticket processing workflow, where tickets are created, Idle Employees register (⌫t, emp, descr) CreateTicket Active tickets logData (tid, emp, id) ResolveTickets h⌫t, empi htid, emp htid,em p htid, empi hempi htid, descri Fig. 2. The control layer of a db-net for ticket manage fresh input variable, and descr is an arbitrary input var Example 3. Figure 2 shows the control layer of a db layer P defined in Example 1 and the data logic layer L control layer realizes a simple ticket processing workfl Idle Employees register (⌫t, emp, descr) CreateTicket Active tickets release (tid, emp) Stall assign (tid, emp) Awake Stalled tickets logData (tid, emp, id) ResolveTickets h⌫t, empi htid, empi htid, empi htid,em pi htid,em pi htid, empi hempi htid, descri htid,em pi Fig. 2. The control layer of a db-net for ticket management. In CreateTicket, ⌫t is a fresh input variable, and descr is an arbitrary input variable. Log hMarcoi Emp Andy Marco Ticket 1 blah 2 blah h1, Andyi Resp Andy 2 Andy 1 h2, Andyi tid = 1 emp = Andy h1, blahi h2, blahi
Run! Idle Employees register (⌫t, emp, descr) CreateTicket Active tickets release (tid, emp) Stall assign (tid, emp) Awake Stalled tickets logData (tid, emp, id) ResolveTickets h⌫t, empi htid, empi htid, empi htid,em pi htid,em pi htid, empi hempi htid, descri htid,em pi Fig. 2. The control layer of a db-net for ticket management. In CreateTicket, ⌫t is a fresh input variable, and descr is an arbitrary input variable. Example 3. Figure 2 shows the control layer of a db-net B, using the persistence layer P defined in Example 1 and the data logic layer L defined in Example 2.2. The control layer realizes a simple ticket processing workflow, where tickets are created, Idle Employees register (⌫t, emp, descr) CreateTicket Active tickets logData (tid, emp, id) ResolveTickets h⌫t, empi htid, emp htid,em p htid, empi hempi htid, descri Fig. 2. The control layer of a db-net for ticket manage fresh input variable, and descr is an arbitrary input var Example 3. Figure 2 shows the control layer of a db layer P defined in Example 1 and the data logic layer L control layer realizes a simple ticket processing workfl Log hMarcoi Emp Andy Marco Ticket 1 blah 2 blah h1, Andyi Resp Andy 2 h2, Andyi tid = 1 emp = Andy h1, blahi h2, blahi

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Cooking with Data and Processes

  • 1. x Cooking with data and processes Marco Montali Free University of Bozen-Bolzano credits to Andy Rivkin
  • 8. General Recipe • Explicit control-flow • Local data (case perspective) • Global data (persistent storage) • Updatable global data • External inputs • Support of automated analysis “REAL” PROCESS
  • 9. Why Automated Analysis? • Classical motivations • Multi-perspective process discovery • Typical approach: different perspectives mined with different techniques • A-posteriori recombination • No guarantee of overall correctness • Verification in the loop!
  • 10. Formal Verification The Conventional, Propositional Case Process control-flow (Un)desired property Abstract model underlying variants of artifact-centric systems. Semantically equivalent to the most expressive models for business proc systems (e.g., GSM). Data Process Data+Process Data Layer: Relational databases / ontologies Data schema, specifying constraints on the allowed states Data instance: state of the DCDS Process Layer: key elements are Atomic actions Condition-action-rules for application of actions Service calls: communication with external environment, new data! alvanese (FUB) Foundations of Data-Aware Process Analysis INRIA Saclay Paris – 18/3/2016
  • 11. (Un)desired property Finite-state transition system Propositional temporal formula|= Formal Verification The Conventional, Propositional Case Process control-flow Abstract model underlying variants of artifact-centric systems. Semantically equivalent to the most expressive models for business proc systems (e.g., GSM). Data Process Data+Process Data Layer: Relational databases / ontologies Data schema, specifying constraints on the allowed states Data instance: state of the DCDS Process Layer: key elements are Atomic actions Condition-action-rules for application of actions Service calls: communication with external environment, new data! alvanese (FUB) Foundations of Data-Aware Process Analysis INRIA Saclay Paris – 18/3/2016
  • 12. (Un)desired property Finite-state transition system Propositional temporal formula|= Formal Verification The Conventional, Propositional Case Process control-flow Verification via model checking 2007 Turing award: Clarke, Emerson, Sifakis Abstract model underlying variants of artifact-centric systems. Semantically equivalent to the most expressive models for business proc systems (e.g., GSM). Data Process Data+Process Data Layer: Relational databases / ontologies Data schema, specifying constraints on the allowed states Data instance: state of the DCDS Process Layer: key elements are Atomic actions Condition-action-rules for application of actions Service calls: communication with external environment, new data! alvanese (FUB) Foundations of Data-Aware Process Analysis INRIA Saclay Paris – 18/3/2016
  • 13. Formal Verification The Data-Aware Case (Un)desired property Data-aware process el underlying variants of artifact-centric systems. quivalent to the most expressive models for business process GSM). Data Process Data+Process elational databases / ontologies ma, specifying constraints on the allowed states nce: state of the DCDS key elements are tions action-rules for application of actions alls: communication with external environment, new data! Foundations of Data-Aware Process Analysis INRIA Saclay Paris – 18/3/2016 (24/1)
  • 14. (Un)desired property First-order temporal formula|= Infinite-state, relational transition system [Vardi 2005] el underlying variants of artifact-centric systems. quivalent to the most expressive models for business process GSM). Data Process Data+Process elational databases / ontologies ma, specifying constraints on the allowed states nce: state of the DCDS key elements are tions action-rules for application of actions alls: communication with external environment, new data! Foundations of Data-Aware Process Analysis INRIA Saclay Paris – 18/3/2016 (24/1) Data-aware process Formal Verification The Data-Aware Case
  • 15. (Un)desired property First-order temporal formula|= ? Infinite-state, relational transition system [Vardi 2005] el underlying variants of artifact-centric systems. quivalent to the most expressive models for business process GSM). Data Process Data+Process elational databases / ontologies ma, specifying constraints on the allowed states nce: state of the DCDS key elements are tions action-rules for application of actions alls: communication with external environment, new data! Foundations of Data-Aware Process Analysis INRIA Saclay Paris – 18/3/2016 (24/1) Data-aware process Formal Verification The Data-Aware Case
  • 16. Why FO Temporal Logics • To inspect data: FO queries • To capture system dynamics: temporal modalities • To track the evolution of objects: FO quantification across states • Example: It is always the case that if an order is order, then that order is eventually either cancelled, or paid and then delivered • N.B.: the interplay between FO quantification and temporal modalities is subtle!
  • 17. BPMNProcesses and Data The process model The data model Modeling and In-Database Management of Relational, Data-Aware Processes start Start Workflow Review Request Fill Reimb. accepted Review Reimb. End Workflow rejected end Pending ID: int empl : string dest : string Accepted ID :int empl : string dest : string cost : int Rejected ID : int empl : string dest : string TrvlMaxAmnt ID : int FID : int maxAmnt : int CurrReq ID: int empl : string dest : string status : string {submitd, acceptd, reimbd, rejd, complete} TrvlCost ID : int FID : int cost : int FK_TrvlMaxAmnt_CurrReq FK_TrvlCost_CurrReq
  • 18. Processes and Data The process model The data model Modeling and In-Database Management of Relational, Data-Aware Processes start Start Workflow Review Request Fill Reimb. accepted Review Reimb. End Workflow rejected end Pending ID: int empl : string dest : string Accepted ID :int empl : string dest : string cost : int Rejected ID : int empl : string dest : string TrvlMaxAmnt ID : int FID : int maxAmnt : int CurrReq ID: int empl : string dest : string status : string {submitd, acceptd, reimbd, rejd, complete} TrvlCost ID : int FID : int cost : int FK_TrvlMaxAmnt_CurrReq FK_TrvlCost_CurrReq BPMN
  • 19. Cooking with BPMN • Explicit control-flow • Local data (case perspective) • Global data (persistent storage) • Updatable global data • External inputs • Support of automated analysis TASTELESS DATA ~ ~
  • 20. Real WfMS Review Request Fill Reim- bursement Review Reim- bursement Rejected Accepted
  • 21. Case and Persistent Data Review Request Fill Reim- bursement Review Reim- bursement Rejected Accepted req info result reimbursement personal info
  • 22. Persistent Data Engineering persistent storage Review Request Fill Reim- bursement Review Reim- bursement Rejected Accepted req info result reimbursement personal info framework data model custom code
  • 23. Case Data Engineering persistent storage Review Request Fill Reim- bursement Review Reim- bursement Rejected Accepted req info result reimbursement personal info framework data model custom code user forms external services
  • 24. Decision Modeling persistent storage Review Request Fill Reim- bursement Review Reim- bursement Rejected Accepted req info result reimbursement personal info framework data model custom code external services 24
  • 25. Cooking with WfMS • Explicit control-flow • Local data (case perspective) • Global data (persistent storage) • Updatable global data • External inputs • Support of automated analysis DATA AS “PROCEDURAL ATTACHMENT" ~ ~
  • 28. Business Entities/Artifacts Data-centric paradigm for process modeling • First: elicitation of relevant business entities that are evolved within given organizational boundaries • Then: definition of the lifecycle of such entities, and how tasks trigger the progression within the lifecycle • Active research area, with concrete languages (e.g., IBM GSM, OMG CMMN) • Cf. EU project ACSI (completed)
  • 29. Cooking with Artifacts • Explicit control-flow • Local data (case perspective) • Global data (persistent storage) • Updatable global data • External inputs • Support of automated analysis TASTELESS FLOWS ~ ~ ~
  • 30. Key Foundational Results Artifact-centric systems can be formalized using Data- Centric Dynamic Systems (DCDSs) [PODS 2013] DCDSs provide: • an execution semantics based on relational transition systems • verification results when the system is studied starting from an initial, fixed instance Confront with the line of research on parameterised verification
  • 31. DCDS Modeling Data layer: persistent data •Relational database with integrity constraints Process layer: evolves the data •Atomic actions: update the data •CA rules: a control-flow component that determines when actions 
 can be executed •Service calls: interact with external world, inject new data! Data-centric Dynamic Systems (DCDSs) [PODS’13] DCDS Data Layer Process Layer ... Services Data layer: maintains data of interest I Relational database with integrity constraints Process layer: evolves the data I Atomic actions: update the data I CA rules: a control-flow component that determines when actions can be executed I Service calls: interact with external world, inject new data! [PODS’13] Bagheri Hariri B., Calvanese D., De Giacomo G., Deutsch A., and Montali M. â ˘AIJVerification of Relational Data-centric Dynamic Systems with External Servicesâ ˘A˙I. In: Proc. of PODS. ACM, 2013, pp. 163â ˘A ¸S174 Modeling, Enactment and Verification of Data-Aware Processes 17 / 55
  • 32. DCDS Modeling “… examine an employee’s trip request and, if accepted, assign the maximum reimbursable amount to it …” Data-centric Dynamic Systems (DCDSs) “. . . examine an employee’s trip request and, if accepted, assign the maximum reimbursable amount to it. . . ” ID Empl Dest Status 1 Bob NY submitted 2 Kriss Rome submitted CurrReq ID FID maxAmnt - – – TrvlMaxAmnt Modeling, Enactment and Verification of Data-Aware Processes 18 / 55
  • 33. DCDS Modeling “… examine an employee’s trip request and, if accepted, assign the maximum reimbursable amount to it …” Data-centric Dynamic Systems (DCDSs) “. . . examine an employee’s trip request and, if accepted, assign the maximum reimbursable amount to it. . . ” ID Empl Dest Status 1 Bob NY submitted 2 Kriss Rome submitted CurrReq ID FID maxAmnt - – – TrvlMaxAmnt Modeling, Enactment and Verification of Data-Aware Processes 18 / 55 Data-centric Dynamic Systems (DCDSs) ID Empl Dest Status 1 Bob NY submitted 2 Kriss Rome submitted CurrReq ID FID maxAmnt - – – TrvlMaxAmnt CA rule: CurrReq(id, e, d, submitted) 7! REVIEWREQUEST(id, e, d) Select the request of Kriss Run REVIEWREQUEST actionExample: ReviewRequest executable for Kriss
  • 34. DCDS Modeling “… examine an employee’s trip request and, if accepted, assign the maximum reimbursable amount to it …” Data-centric Dynamic Systems (DCDSs) “. . . examine an employee’s trip request and, if accepted, assign the maximum reimbursable amount to it. . . ” ID Empl Dest Status 1 Bob NY submitted 2 Kriss Rome submitted CurrReq ID FID maxAmnt - – – TrvlMaxAmnt Modeling, Enactment and Verification of Data-Aware Processes 18 / 55 Example: Data-centric Dynamic Systems (DCDSs) ID Empl Dest Status 1 Bob NY submitted 2 Kriss Rome submitted CurrReq ID FID maxAmnt - – – TrvlMaxAmnt Action REVIEWREQUEST(id, e, d) : 8 >>>< >>>: true del{CurrReq(id, e, d, submitd)} add{CurrReq(id, e, d, status(e, d)), TrvlMaxAmnt(genpk(), id, maxAmnt(e, d))} 9 >>>= >>>; status(Kriss, Rome) = accepted maxAmnt(Kriss, Rome) = 900 genpk() = 10 Update the database Data-centric Dynamic Systems (DCDSs) ID Empl Dest Status 1 Bob NY submitted 2 Kriss Rome accepted CurrReq ID FID maxAmnt 10 2 900 TrvlMaxAmnt
  • 35. DCDS Modeling “… examine an employee’s trip request and, if accepted, assign the maximum reimbursable amount to it …” Data-centric Dynamic Systems (DCDSs) “. . . examine an employee’s trip request and, if accepted, assign the maximum reimbursable amount to it. . . ” ID Empl Dest Status 1 Bob NY submitted 2 Kriss Rome submitted CurrReq ID FID maxAmnt - – – TrvlMaxAmnt Modeling, Enactment and Verification of Data-Aware Processes 18 / 55 Example: Data-centric Dynamic Systems (DCDSs) ID Empl Dest Status 1 Bob NY submitted 2 Kriss Rome submitted CurrReq ID FID maxAmnt - – – TrvlMaxAmnt Action REVIEWREQUEST(id, e, d) : 8 >>>< >>>: true del{CurrReq(id, e, d, submitd)} add{CurrReq(id, e, d, status(e, d)), TrvlMaxAmnt(genpk(), id, maxAmnt(e, d))} 9 >>>= >>>; status(Kriss, Rome) = accepted maxAmnt(Kriss, Rome) = 900 genpk() = 10 Update the database Data-centric Dynamic Systems (DCDSs) ID Empl Dest Status 1 Bob NY submitted 2 Kriss Rome accepted CurrReq ID FID maxAmnt 10 2 900 TrvlMaxAmnt How to model? The Review Request action representation in dapSL RVWREQUES T(id, empl, dest): DELETE FROM CurrReq WHERE CurrReq.id = id; INSERT INTO CurrReq(id, empl, dest, status) VALUES(id, empl, dest, @status(empl, dest)); INSERT INTO TrvlMaxAmnt(tid, tfid, tmaxAmnt) VALUES(@genpk(), id, @maxAmnt(empl, dest)) Modeling, Enactment and Verification of Data-Aware Processes
  • 36. DCDS Enactment DAPHNE: SQL+Java executor for DCDSs [Hopefully CAiSE 2019] • SQL-like frontend • Translation into procedural SQL stored procedures • Management of external services • Execution directly on top of the database, with different execution modalities (including state-space construction) Towards implementing RDSs A RDS model is maintained by the RDBMS (e.g., PostgreSQL) Interaction provided by the Flow Engine, which: I executes calls to the RDBMS I interplays with external services through a Service Manager Modeling, Enactment and Verification of Data-Aware Processes 21 / 55
  • 37. DCDS Verification Highly-undecidable in general Attacked for “state-bounded” systems [PODS 2013] In a state-bounded system, the number of tuples accumulated in a single state cannot exceed a pre-defined bound • Still, infinitely many distinct values can be encountered within and across runs
  • 38. Verification of State-Bounded DCDSs [PODS 2013, Inf. Com. 2018] Branching-time FO logics (contain reachability, soundness,..) • decidable and reducible to standard finite-state model checking
 —> hence, so are soundness properties Linear-time FO logics • Undecidable in general: the system is not Turing-powerful on its own, but the logics isolate runs and separate spurious from genuine ones • Decidable and reducible to standard finite-state model checking by controlling the interplay between FO quantifiers and temporal modalities (object persistency)
  • 39. Is My System State-Bounded? • For a fixed k: decidable. • For “some” bound: undecidable. • Classes with decidable state boundedness • Reasonable for the control layer, not for the data logic [KR2014,ACSD2018,Formal Asp. Comp.2016] • Sufficient, syntactic conditions [PODS2013,KR2014] • Methodologies to guarantee state-boundedness by design [ICSOC2013,CIKM14,STTT16,Formal Asp. Comp.2016]
  • 41. ν-Petri Nets
 [Rosa-Velardo and de Frutos-Escrig, Fund. Inf. 2008] Petri nets with name creation and management • Tokens carry names: ids coming from an infinite domain, can be compared for equality via arc inscriptions • ν variables indicate the generation of fresh names not present in the net • ν-PNs are WSTS —> termination, coverability, boundedness decidable
  • 42. Modeling with ν-Petri Nets
 #1: multiple cases properties that predicate about the evolution of names across states, but we also implicitly isolate a class of DCDSs for which state-boundedness is decidable. This is achieved by leveraging the decidability of state-boundedness for ‹-PNs and the connection provided via the translation. Let us now comment on the modeling power of the Petri net classes presented in this chapter and show how the travel reimbursement example can be represented in di erent contexts using RIAW-nets and ‹-PNs. Get Pending Request Start Workflow Review Request Got Rejected Got Accepted Fill Reimb Review Reimb Reimburse Reject Log Accepted Log Rejected ‹ x x x x x x x x x x x x x x x x x x Financial Accounting manager ‘ ‘ ‘ Figure 5.9: RIAW-net that represents the travel reimbursement process from Section 1.4, extended with a Financial Accounting resource As we have shown in Section5.1.4, RIAW-nets contribute to the concept of Emitter (new case id generation)
  • 43. Modeling with ν-Petri Nets
 #1: multiple cases properties that predicate about the evolution of names across states, but we also implicitly isolate a class of DCDSs for which state-boundedness is decidable. This is achieved by leveraging the decidability of state-boundedness for ‹-PNs and the connection provided via the translation. Let us now comment on the modeling power of the Petri net classes presented in this chapter and show how the travel reimbursement example can be represented in di erent contexts using RIAW-nets and ‹-PNs. Get Pending Request Start Workflow Review Request Got Rejected Got Accepted Fill Reimb Review Reimb Reimburse Reject Log Accepted Log Rejected ‹ x x x x x x x x x x x x x x x x x x Financial Accounting manager ‘ ‘ ‘ Figure 5.9: RIAW-net that represents the travel reimbursement process from Section 1.4, extended with a Financial Accounting resource As we have shown in Section5.1.4, RIAW-nets contribute to the concept of Emitter (new case id generation) req0 req1 req2 req3 req4 req5 req6 … …
  • 44. Modeling with ν-Petri Nets
 #1: multiple cases with resources properties that predicate about the evolution of names across states, but we also implicitly isolate a class of DCDSs for which state-boundedness is decidable. This is achieved by leveraging the decidability of state-boundedness for ‹-PNs and the connection provided via the translation. Let us now comment on the modeling power of the Petri net classes presented in this chapter and show how the travel reimbursement example can be represented in di erent contexts using RIAW-nets and ‹-PNs. Get Pending Request Start Workflow Review Request Got Rejected Got Accepted Fill Reimb Review Reimb Reimburse Reject Log Accepted Log Rejected ‹ x x x x x x x x x x x x x x x x x x Financial Accounting manager ‘ ‘ ‘ Figure 5.9: RIAW-net that represents the travel reimbursement process from Section 1.4, extended with a Financial Accounting resource As we have shown in Section5.1.4, RIAW-nets contribute to the concept of Emitter (new case id generation) req0 req1 req2 req3 req4 req5 req6 … … Resource (bounds simultaneously active cases)
  • 45. Modeling with ν-Petri Nets
 #2: single case with case variables te that in this case the data are abstracted away or, whenever data based diversion nts of the business process flow have to be implemented, “hardcoded” in the kflow definition (for example, Got Rejected represents the failed request approval d models the diversion point of the process in Figure 1.1). Pending Start Workflow CurrReq Submitd Review Request Rejected Review Request Accepted TrvlMax Amount CurrReq Acceptd Fill Reimb TrvlCost CurrReq Complete Review Reimb Rejd Review Reimb Reimbd CurrReq Reimbd CurrReq Rejd Log Accepted Log Rejected x x x x x ‹ ‹ x x ‹ x x x x x x x x x x x gure 5.10: ‹-PN that represents the travel reimbursement process from Section 1.4 y y y y Not very satisfactory: ν variables only useful to represent the generation of new ids
  • 46. Model Ceckhing ν-Petri Nets [Formal Asp. Comp. 2016] Encoding of ν-PNs into DCDSs Bounded ν-PNs translate into state-bounded DCDSs —> An interesting class of DCDSs with decidable state- boundedness <— Verification against FO temporal logics
  • 47. Data Petri Nets [Mannhardt PhD Thesis 2018] • Petri nets with external (case) variables • Each variable has a datatype (with possibly infinite domain) • Transitions read and write variables • Boolean formulae used to express • guards • constraints on value insertion • In [ER 2018] we argued that DPNs elegantly capture decision- aware process models with DMN tables [Batoulis et al, ER 2017] D M Ndiscoverable
  • 48. DPN ExampleM. de Leoni, P. Felli, M. Montali i credit request [amountw ≥ 0] p1 verify [okw ] renegotiate request ! amountr > 15000 ∧ okr == false " skip assessment ! okr == false " p2 simple assessment ! okr == true ∧ okw ∧ amountr < 5000 " advanced assessment ! okr == true ∧ okw ∧ amountr ≥ 5000 " p3 AND split p5p4 open credit loan [okr == true] inform acceptance customer VIP inform acceptance customer normal ! okr == true ∧ amountr < 10000 " inform rejection customer VIP ! okr == true ∧ amountr ≥ 10000 "! okr == false ∧ amountr ≥ 10000 " p7p6 AND join o 1. Our working example of a DPN. Writing operations exist every time guards mention Managing a request with 2 case variables
  • 49. Soundness of DPNs
 with variable-constant conditions [ER 2018] Definition of data-aware soundness (soundness with “some” variable assignment) Data abstraction technique that preserves soundness • Consider representative values instead of actual values • There are finitely many constants used in the net, so representatives are finite • Propositionalize the data and analyze the resulting net Recently extended to variable-variable conditions, using constraints instead of representative values
  • 50. • Implementation in CPN Tools / ProM 1. Translate the DPN into a CPN • Each variable becomes a coloured place • The place always contains a single token tracking the variable value • Read-write operations —> outgoing/incoming arcs 2. Fix the domain of a variable place to contain only its finitely many representatives —> bounded CPN! Soundness of DPNs
 with variable-constant conditions [ER 2018] Soundness Verification of Decision-Aware Process Models 9 i A [xw ] p1 B [xr > 10] C [xr ≤ 10] Fig. 2. Conversion of a simple DPN to CPN (left to right). The green token represents a token with value 0. Arcs without annotations are considered annotated by v• and places with no color are associated with •. Double-headed arcs stand for two arcs with same inscription in both directions.
  • 51. • Implementation in CPN Tools / ProM 1. Translate the DPN into a CPN • Each variable becomes a coloured place • The place always contains a single token tracking the variable value • Read-write operations —> outgoing/incoming arcs 2. Fix the domain of a variable place to contain only its finitely many representatives —> bounded CPN! Soundness of DPNs
 with variable-constant conditions [ER 2018] Soundness Verification of Decision-Aware Process Models 9 i A [xw ] p1 B [xr > 10] C [xr ≤ 10] Fig. 2. Conversion of a simple DPN to CPN (left to right). The green token represents a token with value 0. Arcs without annotations are considered annotated by v• and places with no color are associated with •. Double-headed arcs stand for two arcs with same inscription in both directions. Soundness Verification of Decision-Aware Process Models
  • 52. • Explicit control-flow • Local data (case perspective) • Global data (persistent storage) • Updatable global data • External inputs • Support of automated analysis Cooking with PNs and Cases MISSING KEY INGREDIENTS… ~
  • 53. What about Multiple Cases? • Each case has its own variables • Infinitely many cases may be seen over time • So each case has to carry its own variables! —> Coloured Petri Nets were each 
 “case token” carries a tuple of values • No clear representation of “external” inputs • CPNs usually studied with finite color domains • With unbounded color domains: everything becomes undecidable
  • 54. • Explicit control-flow • Local data (case perspective) • Global data (persistent storage) • Updatable global data • External inputs • Support of automated analysis Cooking with CPNs STILL MISSING KEY INGREDIENTS… ~ ~
  • 56. DB-Nets
 [ToPNoC 2017] Relational DB with constraints Persistence layer “basic” CPN Control layer
  • 57. DB-Nets
 [ToPNoC 2017] Relational DB with constraints Persistence layer “basic” CPN Control layer Data logic layer query action
  • 58. DB-Nets
 [ToPNoC 2017] Relational DB with constraints Persistence layer “basic” CPN Control layer SQL/DCDSquery action view place Data logic layer
  • 59. DB-Nets
 [ToPNoC 2017] Relational DB with constraints Persistence layer “basic” CPN Control layer SQL/DCDSquery action view place a(x,y) action-bound trans. X Data logic layer
  • 60. Example Emp name: string Ticket id: int descr: string Resp emp: string ticket: int Each employee can handle at most one ticket at a given time Log ticket: int emp: string descr: string
  • 61. Example: Queries Get tickets and their description Get “idle” employees plying the update on the given database in over deletions (this is a standard approach situation in which the same fact is asserted The data logic simply exposes a set of q be used by the control layer to obtain data induce updates on the persistence layer. Definition 14 (Data logic layer). Given typed data logic layer over P is a pair hQ, Ai, queries over P; (ii) A is a finite set of action Example 2. We make the scenario of Example layer L over P. L exposes two queries to inspec • Qe(e):- Emp(e) ^ ¬9t.Resp(e, t), to extract id • Qt(t, d):- Ticket(t, d), to extract tickets and t In addition, L provides three main functionalit layer: ticket registration, assignment/release, a alized through four actions (where, for simplic action and its name). The registration of a ne that, given an integer t, and two strings e and ously creates a ticket identified by t and descri assigns the employee identified by e to such tic
  • 62. Example: Actions REGISTER(t,d,e): register ticket t with description d, assigning it to employee e •Add Ticket(t,d), Resp(e,t) ASSIGN(e,t): assign employee e to ticket t •Add Resp(e,t) RELEASE(e,t): release employee e from managing ticket t •Del Resp(e,t) LOG(t,e,d): flush and log the info related to ticket t •Del Ticket(t,d), Resp(e,t) •Add Log(t,e,d)
  • 63. Example Idle Employees register (⌫t, emp, descr) CreateTicket Active tickets release (tid, emp) Stall assign (tid, emp) Awake Stalled tickets logData (tid, emp, id) ResolveTickets h⌫t, empi htid, empi htid, empi htid,em pi htid,em pi htid, empi hempi htid, descri htid,em pi Fig. 2. The control layer of a db-net for ticket management. In CreateTicket, ⌫t is a fresh input variable, and descr is an arbitrary input variable. Example 3. Figure 2 shows the control layer of a db-net B, using the persistence layer P defined in Example 1 and the data logic layer L defined in Example 2.2. The control layer realizes a simple ticket processing workflow, where tickets are created, Idle Employees register (⌫t, emp, descr) CreateTicket Active tickets release (tid, emp) Stall assign (tid, emp) Awake Stalled tickets logData (tid, emp, id) ResolveTickets h⌫t, empi htid, empi htid, empi htid,em pi htid,em pi htid, empi hempi htid, descri htid,em pi Fig. 2. The control layer of a db-net for ticket management. In CreateTicket, ⌫t is a fresh input variable, and descr is an arbitrary input variable. Example 3. Figure 2 shows the control layer of a db-net B, using the persistence layer P defined in Example 1 and the data logic layer L defined in Example 2.2. The control layer realizes a simple ticket processing workflow, where tickets are created, Idle Employees register (⌫t, emp, descr) CreateTicket Active tickets release (tid, emp) Stall assign (tid, emp) Awake Stalled tickets logData (tid, emp, id) ResolveTickets h⌫t, empi htid, empi htid, empi htid,em pi htid,em pi htid, empi hempi htid, descri htid,em pi Fig. 2. The control layer of a db-net for ticket management. In CreateTicket, ⌫t is a fresh input variable, and descr is an arbitrary input variable. Example 3. Figure 2 shows the control layer of a db-net B, using the persistence layer P defined in Example 1 and the data logic layer L defined in Example 2.2. The control layer realizes a simple ticket processing workflow, where tickets are created, Idle Employees register (⌫t, emp, descr) CreateTicket Active tickets release (tid, emp) Stall assign (tid, emp) Awake Stalled tickets logData (tid, emp, id) ResolveTickets h⌫t, empi htid, empi htid, empi htid,em pi htid,em pi htid, empi hempi htid, descri htid,em pi Fig. 2. The control layer of a db-net for ticket management. In CreateTicket, ⌫t is a fresh input variable, and descr is an arbitrary input variable. Example 3. Figure 2 shows the control layer of a db-net B, using the persistence layer P defined in Example 1 and the data logic layer L defined in Example 2.2. The control layer realizes a simple ticket processing workflow, where tickets are created, Idle Employees register (⌫t, emp, descr) CreateTicket Active tickets release (tid, emp) Stall assign (tid, emp) Awake Stalled tickets logData (tid, emp, id) ResolveTickets h⌫t, empi htid, empi htid, empi htid,em pi htid,em pi htid, empi hempi htid, descri htid,em pi Fig. 2. The control layer of a db-net for ticket management. In CreateTicket, ⌫t is a fresh input variable, and descr is an arbitrary input variable. Example 3. Figure 2 shows the control layer of a db-net B, using the persistence layer P defined in Example 1 and the data logic layer L defined in Example 2.2. The control layer realizes a simple ticket processing workflow, where tickets are created, Idle Employees register (⌫t, emp, descr) CreateTicket Active tickets release (tid, emp) Stall assign (tid, emp) Awake Stalled tickets logData (tid, emp, id) ResolveTickets h⌫t, empi htid, empi htid, empi htid,em pi htid,em pi htid, empi hempi htid, descri htid,em pi Fig. 2. The control layer of a db-net for ticket management. In CreateTicket, ⌫t is a fresh input variable, and descr is an arbitrary input variable. Example 3. Figure 2 shows the control layer of a db-net B, using the persistence layer P defined in Example 1 and the data logic layer L defined in Example 2.2. The control layer realizes a simple ticket processing workflow, where tickets are created, Idle Employees register (⌫t, emp, descr) CreateTicket Active tickets logData (tid, emp, id) ResolveTickets h⌫t, empi htid, emp htid,em p htid, empi hempi htid, descri Fig. 2. The control layer of a db-net for ticket manage fresh input variable, and descr is an arbitrary input var Example 3. Figure 2 shows the control layer of a db layer P defined in Example 1 and the data logic layer L control layer realizes a simple ticket processing workfl Idle Employees (⌫t, C Tickets hempi hti Fig. 2. The control fresh input variable, Example 3. Figure layer P defined in E control layer realize int int ⇥ string int ⇥ string ↵✓ can be successfully applied to I if apply(↵✓, I) complies The application of an action instance amounts to ground all in the definition of the action as specified by the given su plying the update on the given database instance, giving p over deletions (this is a standard approach, which unambi situation in which the same fact is asserted to be added and The data logic simply exposes a set of queries and a set be used by the control layer to obtain data from the persi induce updates on the persistence layer. Definition 14 (Data logic layer). Given a D-typed persi typed data logic layer over P is a pair hQ, Ai, where: (i) Q is queries over P; (ii) A is a finite set of actions over P. Example 2. We make the scenario of Example 1 operational, in layer L over P. L exposes two queries to inspect the persistence • Qe(e):- Emp(e) ^ ¬9t.Resp(e, t), to extract idle employees;Qt(t, d):- Ticket(t, d), to extract tickets and their description. n addition, L provides three main functionalities to manipulate tickets in persistence ayer: ticket registration, assignment/release, and logging. Such functionalities are re- lized through four actions (where, for simplicity, we blur the distinction between an ction and its name). The registration of a new ticket is managed by an action reg hat, given an integer t, and two strings e and d, (reg·params = ht, e, di, simultane- Case variables: - ticket id - name of responsible employee int ⇥ string
  • 64. Example Idle Employees register (⌫t, emp, descr) CreateTicket Active tickets release (tid, emp) Stall assign (tid, emp) Awake Stalled tickets logData (tid, emp, id) ResolveTickets h⌫t, empi htid, empi htid, empi htid,em pi htid,em pi htid, empi hempi htid, descri htid,em pi Fig. 2. The control layer of a db-net for ticket management. In CreateTicket, ⌫t is a fresh input variable, and descr is an arbitrary input variable. Example 3. Figure 2 shows the control layer of a db-net B, using the persistence layer P defined in Example 1 and the data logic layer L defined in Example 2.2. The control layer realizes a simple ticket processing workflow, where tickets are created, Idle Employees register (⌫t, emp, descr) CreateTicket Active tickets logData (tid, emp, id) ResolveTickets h⌫t, empi htid, emp htid,em p htid, empi hempi htid, descri Fig. 2. The control layer of a db-net for ticket manage fresh input variable, and descr is an arbitrary input var Example 3. Figure 2 shows the control layer of a db layer P defined in Example 1 and the data logic layer L control layer realizes a simple ticket processing workfl Idle Employees register (⌫t, emp, descr) CreateTicket Active tickets release (tid, emp) Stall assign (tid, emp) Awake Stalled tickets logData (tid, emp, id) ResolveTickets h⌫t, empi htid, empi htid, empi htid,em pi htid,em pi htid, empi hempi htid, descri htid,em pi Fig. 2. The control layer of a db-net for ticket management. In CreateTicket, ⌫t is a fresh input variable, and descr is an arbitrary input variable. Example 3. Figure 2 shows the control layer of a db-net B, using the persistence layer P defined in Example 1 and the data logic layer L defined in Example 2.2. The control layer realizes a simple ticket processing workflow, where tickets are created, Idle Employees register (⌫t, emp, descr) CreateTicket Active tickets release (tid, emp) Stall assign (tid, emp) Awake Stalled tickets logData (tid, emp, id) ResolveTickets h⌫t, empi htid, empi htid, empi htid,em pi htid,em pi htid, empi hempi htid, descri htid,em pi Fig. 2. The control layer of a db-net for ticket management. In CreateTicket, ⌫t is a fresh input variable, and descr is an arbitrary input variable. Example 3. Figure 2 shows the control layer of a db-net B, using the persistence layer P defined in Example 1 and the data logic layer L defined in Example 2.2. The control layer realizes a simple ticket processing workflow, where tickets are created, Idle Employees register (⌫t, emp, descr) CreateTicket Active tickets release (tid, emp) Stall assign (tid, emp) Awake Stalled tickets logData (tid, emp, id) ResolveTickets h⌫t, empi htid, empi htid, empi htid,em pi htid,em pi htid, empi hempi htid, descri htid,em pi Fig. 2. The control layer of a db-net for ticket management. In CreateTicket, ⌫t is a fresh input variable, and descr is an arbitrary input variable. Example 3. Figure 2 shows the control layer of a db-net B, using the persistence layer P defined in Example 1 and the data logic layer L defined in Example 2.2. The control layer realizes a simple ticket processing workflow, where tickets are created, Idle Employees (⌫t, C Tickets hempi hti Fig. 2. The control fresh input variable, Example 3. Figure layer P defined in E control layer realize
  • 65. Example Idle Employees register (⌫t, emp, descr) CreateTicket Active tickets release (tid, emp) Stall assign (tid, emp) Awake Stalled tickets logData (tid, emp, id) ResolveTickets h⌫t, empi htid, empi htid, empi htid,em pi htid,em pi htid, empi hempi htid, descri htid,em pi Fig. 2. The control layer of a db-net for ticket management. In CreateTicket, ⌫t is a fresh input variable, and descr is an arbitrary input variable. Example 3. Figure 2 shows the control layer of a db-net B, using the persistence layer P defined in Example 1 and the data logic layer L defined in Example 2.2. The control layer realizes a simple ticket processing workflow, where tickets are created, Idle Employees register (⌫t, emp, descr) CreateTicket Active tickets logData (tid, emp, id) ResolveTickets h⌫t, empi htid, emp htid,em p htid, empi hempi htid, descri Fig. 2. The control layer of a db-net for ticket manage fresh input variable, and descr is an arbitrary input var Example 3. Figure 2 shows the control layer of a db layer P defined in Example 1 and the data logic layer L control layer realizes a simple ticket processing workfl Idle Employees register (⌫t, emp, descr) CreateTicket Active tickets release (tid, emp) Stall assign (tid, emp) Awake Stalled tickets logData (tid, emp, id) ResolveTickets h⌫t, empi htid, empi htid, empi htid,em pi htid,em pi htid, empi hempi htid, descri htid,em pi Fig. 2. The control layer of a db-net for ticket management. In CreateTicket, ⌫t is a fresh input variable, and descr is an arbitrary input variable.
  • 66. Example Idle Employees register (⌫t, emp, descr) CreateTicket Active tickets release (tid, emp) Stall assign (tid, emp) Awake Stalled tickets logData (tid, emp, id) ResolveTickets h⌫t, empi htid, empi htid, empi htid,em pi htid,em pi htid, empi hempi htid, descri htid,em pi Fig. 2. The control layer of a db-net for ticket management. In CreateTicket, ⌫t is a fresh input variable, and descr is an arbitrary input variable. Example 3. Figure 2 shows the control layer of a db-net B, using the persistence layer P defined in Example 1 and the data logic layer L defined in Example 2.2. The control layer realizes a simple ticket processing workflow, where tickets are created, Idle Employees register (⌫t, emp, descr) CreateTicket Active tickets logData (tid, emp, id) ResolveTickets h⌫t, empi htid, emp htid,em p htid, empi hempi htid, descri Fig. 2. The control layer of a db-net for ticket manage fresh input variable, and descr is an arbitrary input var Example 3. Figure 2 shows the control layer of a db layer P defined in Example 1 and the data logic layer L control layer realizes a simple ticket processing workfl Idle Employees register (⌫t, emp, descr) CreateTicket Active tickets release (tid, emp) Stall assign (tid, emp) Awake Stalled tickets logData (tid, emp, id) ResolveTickets h⌫t, empi htid, empi htid, empi htid,em pi htid,em pi htid, empi hempi htid, descri htid,em pi Fig. 2. The control layer of a db-net for ticket management. In CreateTicket, ⌫t is a fresh input variable, and descr is an arbitrary input variable.
  • 67. Run! Idle Employees register (⌫t, emp, descr) CreateTicket Active tickets release (tid, emp) Stall assign (tid, emp) Awake Stalled tickets logData (tid, emp, id) ResolveTickets h⌫t, empi htid, empi htid, empi htid,em pi htid,em pi htid, empi hempi htid, descri htid,em pi Fig. 2. The control layer of a db-net for ticket management. In CreateTicket, ⌫t is a fresh input variable, and descr is an arbitrary input variable. Example 3. Figure 2 shows the control layer of a db-net B, using the persistence layer P defined in Example 1 and the data logic layer L defined in Example 2.2. The control layer realizes a simple ticket processing workflow, where tickets are created, Idle Employees register (⌫t, emp, descr) CreateTicket Active tickets logData (tid, emp, id) ResolveTickets h⌫t, empi htid, emp htid,em p htid, empi hempi htid, descri Fig. 2. The control layer of a db-net for ticket manage fresh input variable, and descr is an arbitrary input var Example 3. Figure 2 shows the control layer of a db layer P defined in Example 1 and the data logic layer L control layer realizes a simple ticket processing workfl Idle Employees register (⌫t, emp, descr) CreateTicket Active tickets release (tid, emp) Stall assign (tid, emp) Awake Stalled tickets logData (tid, emp, id) ResolveTickets h⌫t, empi htid, empi htid, empi htid,em pi htid,em pi htid, empi hempi htid, descri htid,em pi Fig. 2. The control layer of a db-net for ticket management. In CreateTicket, ⌫t is a fresh input variable, and descr is an arbitrary input variable. Emp Andy Marco TicketResp Log hAndyi hMarcoi
  • 68. Run! Idle Employees register (⌫t, emp, descr) CreateTicket Active tickets release (tid, emp) Stall assign (tid, emp) Awake Stalled tickets logData (tid, emp, id) ResolveTickets h⌫t, empi htid, empi htid, empi htid,em pi htid,em pi htid, empi hempi htid, descri htid,em pi Fig. 2. The control layer of a db-net for ticket management. In CreateTicket, ⌫t is a fresh input variable, and descr is an arbitrary input variable. Example 3. Figure 2 shows the control layer of a db-net B, using the persistence layer P defined in Example 1 and the data logic layer L defined in Example 2.2. The control layer realizes a simple ticket processing workflow, where tickets are created, Idle Employees register (⌫t, emp, descr) CreateTicket Active tickets logData (tid, emp, id) ResolveTickets h⌫t, empi htid, emp htid,em p htid, empi hempi htid, descri Fig. 2. The control layer of a db-net for ticket manage fresh input variable, and descr is an arbitrary input var Example 3. Figure 2 shows the control layer of a db layer P defined in Example 1 and the data logic layer L control layer realizes a simple ticket processing workfl Idle Employees register (⌫t, emp, descr) CreateTicket Active tickets release (tid, emp) Stall assign (tid, emp) Awake Stalled tickets logData (tid, emp, id) ResolveTickets h⌫t, empi htid, empi htid, empi htid,em pi htid,em pi htid, empi hempi htid, descri htid,em pi Fig. 2. The control layer of a db-net for ticket management. In CreateTicket, ⌫t is a fresh input variable, and descr is an arbitrary input variable. TicketResp Log hAndyi hMarcoi ⌫t = 1 emp = Andy descr = blah Emp Andy Marco
  • 69. Run! Idle Employees register (⌫t, emp, descr) CreateTicket Active tickets release (tid, emp) Stall assign (tid, emp) Awake Stalled tickets logData (tid, emp, id) ResolveTickets h⌫t, empi htid, empi htid, empi htid,em pi htid,em pi htid, empi hempi htid, descri htid,em pi Fig. 2. The control layer of a db-net for ticket management. In CreateTicket, ⌫t is a fresh input variable, and descr is an arbitrary input variable. Example 3. Figure 2 shows the control layer of a db-net B, using the persistence layer P defined in Example 1 and the data logic layer L defined in Example 2.2. The control layer realizes a simple ticket processing workflow, where tickets are created, Idle Employees register (⌫t, emp, descr) CreateTicket Active tickets logData (tid, emp, id) ResolveTickets h⌫t, empi htid, emp htid,em p htid, empi hempi htid, descri Fig. 2. The control layer of a db-net for ticket manage fresh input variable, and descr is an arbitrary input var Example 3. Figure 2 shows the control layer of a db layer P defined in Example 1 and the data logic layer L control layer realizes a simple ticket processing workfl Idle Employees register (⌫t, emp, descr) CreateTicket Active tickets release (tid, emp) Stall assign (tid, emp) Awake Stalled tickets logData (tid, emp, id) ResolveTickets h⌫t, empi htid, empi htid, empi htid,em pi htid,em pi htid, empi hempi htid, descri htid,em pi Fig. 2. The control layer of a db-net for ticket management. In CreateTicket, ⌫t is a fresh input variable, and descr is an arbitrary input variable. Ticket 1 blah Resp Andy 1 Log hMarcoi Emp Andy Marco h1, Andyi
  • 70. Run! Idle Employees register (⌫t, emp, descr) CreateTicket Active tickets release (tid, emp) Stall assign (tid, emp) Awake Stalled tickets logData (tid, emp, id) ResolveTickets h⌫t, empi htid, empi htid, empi htid,em pi htid,em pi htid, empi hempi htid, descri htid,em pi Fig. 2. The control layer of a db-net for ticket management. In CreateTicket, ⌫t is a fresh input variable, and descr is an arbitrary input variable. Example 3. Figure 2 shows the control layer of a db-net B, using the persistence layer P defined in Example 1 and the data logic layer L defined in Example 2.2. The control layer realizes a simple ticket processing workflow, where tickets are created, Idle Employees register (⌫t, emp, descr) CreateTicket Active tickets logData (tid, emp, id) ResolveTickets h⌫t, empi htid, emp htid,em p htid, empi hempi htid, descri Fig. 2. The control layer of a db-net for ticket manage fresh input variable, and descr is an arbitrary input var Example 3. Figure 2 shows the control layer of a db layer P defined in Example 1 and the data logic layer L control layer realizes a simple ticket processing workfl Idle Employees register (⌫t, emp, descr) CreateTicket Active tickets release (tid, emp) Stall assign (tid, emp) Awake Stalled tickets logData (tid, emp, id) ResolveTickets h⌫t, empi htid, empi htid, empi htid,em pi htid,em pi htid, empi hempi htid, descri htid,em pi Fig. 2. The control layer of a db-net for ticket management. In CreateTicket, ⌫t is a fresh input variable, and descr is an arbitrary input variable. Ticket 1 blah Resp Andy 1 Log hMarcoi Emp Andy Marco h1, Andyi tid = 1 emp = Andy
  • 71. Run! Idle Employees register (⌫t, emp, descr) CreateTicket Active tickets release (tid, emp) Stall assign (tid, emp) Awake Stalled tickets logData (tid, emp, id) ResolveTickets h⌫t, empi htid, empi htid, empi htid,em pi htid,em pi htid, empi hempi htid, descri htid,em pi Fig. 2. The control layer of a db-net for ticket management. In CreateTicket, ⌫t is a fresh input variable, and descr is an arbitrary input variable. Example 3. Figure 2 shows the control layer of a db-net B, using the persistence layer P defined in Example 1 and the data logic layer L defined in Example 2.2. The control layer realizes a simple ticket processing workflow, where tickets are created, Idle Employees register (⌫t, emp, descr) CreateTicket Active tickets logData (tid, emp, id) ResolveTickets h⌫t, empi htid, emp htid,em p htid, empi hempi htid, descri Fig. 2. The control layer of a db-net for ticket manage fresh input variable, and descr is an arbitrary input var Example 3. Figure 2 shows the control layer of a db layer P defined in Example 1 and the data logic layer L control layer realizes a simple ticket processing workfl Idle Employees register (⌫t, emp, descr) CreateTicket Active tickets release (tid, emp) Stall assign (tid, emp) Awake Stalled tickets logData (tid, emp, id) ResolveTickets h⌫t, empi htid, empi htid, empi htid,em pi htid,em pi htid, empi hempi htid, descri htid,em pi Fig. 2. The control layer of a db-net for ticket management. In CreateTicket, ⌫t is a fresh input variable, and descr is an arbitrary input variable. Resp Log hAndyi hMarcoi Emp Andy Marco Ticket 1 blah h1, blahi h1, Andyi
  • 72. DB-nets Enactment CPN Tools extension (works also for the previous PN models we have seen)
  • 73. Verification of DB-nets Encoding of DB-nets into DCDSs Bounded DB-nets translate into state-bounded DCDSs <— Verification against FO temporal logics, limited to string
 and real domains without arithmetics —> Unfortunately, CPN Tools does not allow to extend the
 state space construction mechanism • We have provided an encoding from DB-nets to CPNs, restricting the query language to UCQs with filters
  • 74. • Explicit control-flow • Local data (case perspective) • Global data (persistent storage) • Updatable global data • External inputs • Support of automated analysis Cooking with DB-Nets FINALLY!
  • 75. Conclusion A cookbook for integrated models of data and processes • with increasing sophistication of the data component • paying attention to modeling, enactment, and automated analysis Which models for which scenarios? • Application of DB-nets to application integration patterns [EDOC 2018] Some open points for modeling • Arithmetics? • Connection with front-end languages 
 (data-aware BPMN soon to come) • Connection with interaction models (Proclets)
  • 76. Parameterized Verification Split the persistent storage into a read-only database, and a read- write working memory Verification “irrespectively” of the content of the read-only DB • Extensively studied in database theory (Deutsch, Hull, Vianu, …) • by carefully limiting the expressive power of all components (fragile setting) • by considering arithmetic in the picture • Recently attacked by us using SMT model checking for array- based systems
  • 77. Declarative Data-Aware Processes: the OCBC Approach 10 A. Artale, D. Calvanese, M. Montali, and W. van der Aalst is about 1 1 creates 1 promotes 1 creates 1 1 stops 1 closes 1 Person Candidate Application Job Offer Job Profile1 / made by 1..⇤ ⇤ responds to 1 ⇤ refers to 1 register data submit mark as eligible post offer cancel hiring determine winner Fig. 5: Activity-object relationships in the job hiring scenario of Section 2.1
  • 78. Declarative Data-Aware Processes: the OCBC Approach 10 A. Artale, D. Calvanese, M. Montali, and W. van der Aalst is about 1 1 creates 1 promotes 1 creates 1 1 stops 1 closes 1 Person Candidate Application Job Offer Job Profile1 / made by 1..⇤ ⇤ responds to 1 ⇤ refers to 1 register data submit mark as eligible post offer cancel hiring determine winner Fig. 5: Activity-object relationships in the job hiring scenario of Section 2.1
  • 79. Declarative Data-Aware Processes: the OCBC Approach Enriching Data Models with Behavioral Constraints 13 is about 1 1 creates 1 promotes 1 creates 1 1 stops 1 closes 1 Person Candidate Application Job Offer Job Profile1 / made by 1..⇤ ⇤ responds to 1 ⇤ refers to 1 register data submit mark as eligible post offer cancel hiring determine winner Fig. 7: OCBC model for the job hiring scenario of Section 2.1, where each one of C.1–C.11 therein corresponds to either an activity-object relationship or a co-reference temporal constraint in the OCBC model. The lightweight constraint is redundant: it is implied by the other constraints in the diagram. Formal semantics with temporal description logics (Satisfiability EXPTIME-complete)
  • 80.
  • 82. ExampleProcesses and Data The process model The data model Modeling and In-Database Management of Relational, Data-Aware Processes start Start Workflow Review Request Fill Reimb. accepted Review Reimb. End Workflow rejected end Pending ID: int empl : string dest : string Accepted ID :int empl : string dest : string cost : int Rejected ID : int empl : string dest : string TrvlMaxAmnt ID : int FID : int maxAmnt : int CurrReq ID: int empl : string dest : string status : string {submitd, acceptd, reimbd, rejd, complete} TrvlCost ID : int FID : int cost : int FK_TrvlMaxAmnt_CurrReq FK_TrvlCost_CurrReq start workflow submitted request review request <ν,e,d,“submitted”> <id,e,d,status><e,d>
  • 83. ExampleProcesses and Data The process model The data model Modeling and In-Database Management of Relational, Data-Aware Processes start Start Workflow Review Request Fill Reimb. accepted Review Reimb. End Workflow rejected end Pending ID: int empl : string dest : string Accepted ID :int empl : string dest : string cost : int Rejected ID : int empl : string dest : string TrvlMaxAmnt ID : int FID : int maxAmnt : int CurrReq ID: int empl : string dest : string status : string {submitd, acceptd, reimbd, rejd, complete} TrvlCost ID : int FID : int cost : int FK_TrvlMaxAmnt_CurrReq FK_TrvlCost_CurrReq start workflow submitted request review request <ν,e,d,“submitted”> <id,e,d,status>
  • 84. Persistence Layer Typed relational DB with constraints • DB: set of relation schemas with typed components • Type: data domain with rigidly defined predicates • Constraints: Domain-independent FO sentences • Keys, FKs, dependencies, multiplicities, … DB Instance: finite set of typed facts over DB, satisfying all constraints
  • 85. Example Emp name: string Ticket id: int descr: string Resp emp: string ticket: int Each employee can handle at most one ticket at a given time Log ticket: int emp: string descr: string
  • 86. Example: Queries • Get tickets and their description • Get “idle” employees plying the update on the given database in over deletions (this is a standard approach situation in which the same fact is asserted The data logic simply exposes a set of q be used by the control layer to obtain data induce updates on the persistence layer. Definition 14 (Data logic layer). Given typed data logic layer over P is a pair hQ, Ai, queries over P; (ii) A is a finite set of action Example 2. We make the scenario of Example layer L over P. L exposes two queries to inspec • Qe(e):- Emp(e) ^ ¬9t.Resp(e, t), to extract id • Qt(t, d):- Ticket(t, d), to extract tickets and t In addition, L provides three main functionalit layer: ticket registration, assignment/release, a alized through four actions (where, for simplic action and its name). The registration of a ne that, given an integer t, and two strings e and ously creates a ticket identified by t and descri assigns the employee identified by e to such tic
  • 87. Example: Actions REGISTER(t,d,e): register ticket t with description d, assigning it to employee e •Add Ticket(t,d), Resp(e,t) ASSIGN(e,t): assign employee e to ticket t •Add Resp(e,t) RELEASE(e,t): release employee e from managing ticket t •Del Resp(e,t) LOG(t,e,d): flush and log the info related to ticket t •Del Ticket(t,d), Resp(e,t) •Add Log(t,e,d)
  • 88. Control Layer A “data-oriented” CPN • Process control-flow • Evolution of tokens and their “case” data • Interaction with the persistence layer via the data logic layer
  • 89. Normal Place • Represent case states and resources • Color: schema of the local data carried by tokens • May be seen as a special relation of the persistence layer • Tokens explicitly manipulated by the control layer, as customary in CPNs Tickets h Fig. 2. Th
  • 90. View Place • “View” of the persistence layer provided to the control layer • Hosts the answers to a query from the data logic • Clearly identifies where the control layer needs to “read” from the persistence layer • Not modified explicitly by the control layer • Implicitly updated by applying actions on the persistence layer, and recomputing the view Tickets h Fig. 2. Th
  • 91. Example Idle Employees register (⌫t, emp, descr) CreateTicket Active tickets release (tid, emp) Stall assign (tid, emp) Awake Stalled tickets logData (tid, emp, id) ResolveTickets h⌫t, empi htid, empi htid, empi htid,em pi htid,em pi htid, empi hempi htid, descri htid,em pi Fig. 2. The control layer of a db-net for ticket management. In CreateTicket, ⌫t is a fresh input variable, and descr is an arbitrary input variable. Example 3. Figure 2 shows the control layer of a db-net B, using the persistence layer P defined in Example 1 and the data logic layer L defined in Example 2.2. The control layer realizes a simple ticket processing workflow, where tickets are created, Idle Employees register (⌫t, emp, descr) CreateTicket Active tickets release (tid, emp) Stall assign (tid, emp) Awake Stalled tickets logData (tid, emp, id) ResolveTickets h⌫t, empi htid, empi htid, empi htid,em pi htid,em pi htid, empi hempi htid, descri htid,em pi Fig. 2. The control layer of a db-net for ticket management. In CreateTicket, ⌫t is a fresh input variable, and descr is an arbitrary input variable. Example 3. Figure 2 shows the control layer of a db-net B, using the persistence layer P defined in Example 1 and the data logic layer L defined in Example 2.2. The control layer realizes a simple ticket processing workflow, where tickets are created, Idle Employees register (⌫t, emp, descr) CreateTicket Active tickets release (tid, emp) Stall assign (tid, emp) Awake Stalled tickets logData (tid, emp, id) ResolveTickets h⌫t, empi htid, empi htid, empi htid,em pi htid,em pi htid, empi hempi htid, descri htid,em pi Fig. 2. The control layer of a db-net for ticket management. In CreateTicket, ⌫t is a fresh input variable, and descr is an arbitrary input variable. Example 3. Figure 2 shows the control layer of a db-net B, using the persistence layer P defined in Example 1 and the data logic layer L defined in Example 2.2. The control layer realizes a simple ticket processing workflow, where tickets are created, Idle Employees register (⌫t, emp, descr) CreateTicket Active tickets release (tid, emp) Stall assign (tid, emp) Awake Stalled tickets logData (tid, emp, id) ResolveTickets h⌫t, empi htid, empi htid, empi htid,em pi htid,em pi htid, empi hempi htid, descri htid,em pi Fig. 2. The control layer of a db-net for ticket management. In CreateTicket, ⌫t is a fresh input variable, and descr is an arbitrary input variable. Example 3. Figure 2 shows the control layer of a db-net B, using the persistence layer P defined in Example 1 and the data logic layer L defined in Example 2.2. The control layer realizes a simple ticket processing workflow, where tickets are created, Idle Employees register (⌫t, emp, descr) CreateTicket Active tickets release (tid, emp) Stall assign (tid, emp) Awake Stalled tickets logData (tid, emp, id) ResolveTickets h⌫t, empi htid, empi htid, empi htid,em pi htid,em pi htid, empi hempi htid, descri htid,em pi Fig. 2. The control layer of a db-net for ticket management. In CreateTicket, ⌫t is a fresh input variable, and descr is an arbitrary input variable. Example 3. Figure 2 shows the control layer of a db-net B, using the persistence layer P defined in Example 1 and the data logic layer L defined in Example 2.2. The control layer realizes a simple ticket processing workflow, where tickets are created, Idle Employees register (⌫t, emp, descr) CreateTicket Active tickets release (tid, emp) Stall assign (tid, emp) Awake Stalled tickets logData (tid, emp, id) ResolveTickets h⌫t, empi htid, empi htid, empi htid,em pi htid,em pi htid, empi hempi htid, descri htid,em pi Fig. 2. The control layer of a db-net for ticket management. In CreateTicket, ⌫t is a fresh input variable, and descr is an arbitrary input variable. Example 3. Figure 2 shows the control layer of a db-net B, using the persistence layer P defined in Example 1 and the data logic layer L defined in Example 2.2. The control layer realizes a simple ticket processing workflow, where tickets are created, Idle Employees register (⌫t, emp, descr) CreateTicket Active tickets logData (tid, emp, id) ResolveTickets h⌫t, empi htid, emp htid,em p htid, empi hempi htid, descri Fig. 2. The control layer of a db-net for ticket manage fresh input variable, and descr is an arbitrary input var Example 3. Figure 2 shows the control layer of a db layer P defined in Example 1 and the data logic layer L control layer realizes a simple ticket processing workfl Idle Employees (⌫t, C Tickets hempi hti Fig. 2. The control fresh input variable, Example 3. Figure layer P defined in E control layer realize int int ⇥ string int ⇥ string ↵✓ can be successfully applied to I if apply(↵✓, I) complies The application of an action instance amounts to ground all in the definition of the action as specified by the given su plying the update on the given database instance, giving p over deletions (this is a standard approach, which unambi situation in which the same fact is asserted to be added and The data logic simply exposes a set of queries and a set be used by the control layer to obtain data from the persi induce updates on the persistence layer. Definition 14 (Data logic layer). Given a D-typed persi typed data logic layer over P is a pair hQ, Ai, where: (i) Q is queries over P; (ii) A is a finite set of actions over P. Example 2. We make the scenario of Example 1 operational, in layer L over P. L exposes two queries to inspect the persistence • Qe(e):- Emp(e) ^ ¬9t.Resp(e, t), to extract idle employees;Qt(t, d):- Ticket(t, d), to extract tickets and their description. n addition, L provides three main functionalities to manipulate tickets in persistence ayer: ticket registration, assignment/release, and logging. Such functionalities are re- lized through four actions (where, for simplicity, we blur the distinction between an ction and its name). The registration of a new ticket is managed by an action reg hat, given an integer t, and two strings e and d, (reg·params = ht, e, di, simultane- Case variables: - ticket id - name of responsible employee int ⇥ string
  • 92. Transition Atomic unit of work within the control layer • Input data: obtained by • Consuming tokens from its input places • Reading tokens from its input view places • To access tokens and their data: multisets of tuples of “matching” variables • Data guard over the input variables
  • 93. Transition Atomic unit of work within the control layer • Output data: inputs + additional variables (external input) + ν variables (new ids) • Output data used to • Bind to an action of the data logic, updating the persistence layer • Produce tokens and insert them into the output places
  • 94. Example Idle Employees register (⌫t, emp, descr) CreateTicket Active tickets release (tid, emp) Stall assign (tid, emp) Awake Stalled tickets logData (tid, emp, id) ResolveTickets h⌫t, empi htid, empi htid, empi htid,em pi htid,em pi htid, empi hempi htid, descri htid,em pi Fig. 2. The control layer of a db-net for ticket management. In CreateTicket, ⌫t is a fresh input variable, and descr is an arbitrary input variable. Example 3. Figure 2 shows the control layer of a db-net B, using the persistence layer P defined in Example 1 and the data logic layer L defined in Example 2.2. The control layer realizes a simple ticket processing workflow, where tickets are created, Idle Employees register (⌫t, emp, descr) CreateTicket Active tickets logData (tid, emp, id) ResolveTickets h⌫t, empi htid, emp htid,em p htid, empi hempi htid, descri Fig. 2. The control layer of a db-net for ticket manage fresh input variable, and descr is an arbitrary input var Example 3. Figure 2 shows the control layer of a db layer P defined in Example 1 and the data logic layer L control layer realizes a simple ticket processing workfl Idle Employees register (⌫t, emp, descr) CreateTicket Active tickets release (tid, emp) Stall assign (tid, emp) Awake Stalled tickets logData (tid, emp, id) ResolveTickets h⌫t, empi htid, empi htid, empi htid,em pi htid,em pi htid, empi hempi htid, descri htid,em pi Fig. 2. The control layer of a db-net for ticket management. In CreateTicket, ⌫t is a fresh input variable, and descr is an arbitrary input variable. Example 3. Figure 2 shows the control layer of a db-net B, using the persistence layer P defined in Example 1 and the data logic layer L defined in Example 2.2. The control layer realizes a simple ticket processing workflow, where tickets are created, Idle Employees register (⌫t, emp, descr) CreateTicket Active tickets release (tid, emp) Stall assign (tid, emp) Awake Stalled tickets logData (tid, emp, id) ResolveTickets h⌫t, empi htid, empi htid, empi htid,em pi htid,em pi htid, empi hempi htid, descri htid,em pi Fig. 2. The control layer of a db-net for ticket management. In CreateTicket, ⌫t is a fresh input variable, and descr is an arbitrary input variable. Example 3. Figure 2 shows the control layer of a db-net B, using the persistence layer P defined in Example 1 and the data logic layer L defined in Example 2.2. The control layer realizes a simple ticket processing workflow, where tickets are created, Idle Employees register (⌫t, emp, descr) CreateTicket Active tickets release (tid, emp) Stall assign (tid, emp) Awake Stalled tickets logData (tid, emp, id) ResolveTickets h⌫t, empi htid, empi htid, empi htid,em pi htid,em pi htid, empi hempi htid, descri htid,em pi Fig. 2. The control layer of a db-net for ticket management. In CreateTicket, ⌫t is a fresh input variable, and descr is an arbitrary input variable. Example 3. Figure 2 shows the control layer of a db-net B, using the persistence layer P defined in Example 1 and the data logic layer L defined in Example 2.2. The control layer realizes a simple ticket processing workflow, where tickets are created, Idle Employees (⌫t, C Tickets hempi hti Fig. 2. The control fresh input variable, Example 3. Figure layer P defined in E control layer realize
  • 95. Example Idle Employees register (⌫t, emp, descr) CreateTicket Active tickets release (tid, emp) Stall assign (tid, emp) Awake Stalled tickets logData (tid, emp, id) ResolveTickets h⌫t, empi htid, empi htid, empi htid,em pi htid,em pi htid, empi hempi htid, descri htid,em pi Fig. 2. The control layer of a db-net for ticket management. In CreateTicket, ⌫t is a fresh input variable, and descr is an arbitrary input variable. Example 3. Figure 2 shows the control layer of a db-net B, using the persistence layer P defined in Example 1 and the data logic layer L defined in Example 2.2. The control layer realizes a simple ticket processing workflow, where tickets are created, Idle Employees register (⌫t, emp, descr) CreateTicket Active tickets logData (tid, emp, id) ResolveTickets h⌫t, empi htid, emp htid,em p htid, empi hempi htid, descri Fig. 2. The control layer of a db-net for ticket manage fresh input variable, and descr is an arbitrary input var Example 3. Figure 2 shows the control layer of a db layer P defined in Example 1 and the data logic layer L control layer realizes a simple ticket processing workfl Idle Employees register (⌫t, emp, descr) CreateTicket Active tickets release (tid, emp) Stall assign (tid, emp) Awake Stalled tickets logData (tid, emp, id) ResolveTickets h⌫t, empi htid, empi htid, empi htid,em pi htid,em pi htid, empi hempi htid, descri htid,em pi Fig. 2. The control layer of a db-net for ticket management. In CreateTicket, ⌫t is a fresh input variable, and descr is an arbitrary input variable.
  • 96. Example Idle Employees register (⌫t, emp, descr) CreateTicket Active tickets release (tid, emp) Stall assign (tid, emp) Awake Stalled tickets logData (tid, emp, id) ResolveTickets h⌫t, empi htid, empi htid, empi htid,em pi htid,em pi htid, empi hempi htid, descri htid,em pi Fig. 2. The control layer of a db-net for ticket management. In CreateTicket, ⌫t is a fresh input variable, and descr is an arbitrary input variable. Example 3. Figure 2 shows the control layer of a db-net B, using the persistence layer P defined in Example 1 and the data logic layer L defined in Example 2.2. The control layer realizes a simple ticket processing workflow, where tickets are created, Idle Employees register (⌫t, emp, descr) CreateTicket Active tickets logData (tid, emp, id) ResolveTickets h⌫t, empi htid, emp htid,em p htid, empi hempi htid, descri Fig. 2. The control layer of a db-net for ticket manage fresh input variable, and descr is an arbitrary input var Example 3. Figure 2 shows the control layer of a db layer P defined in Example 1 and the data logic layer L control layer realizes a simple ticket processing workfl Idle Employees register (⌫t, emp, descr) CreateTicket Active tickets release (tid, emp) Stall assign (tid, emp) Awake Stalled tickets logData (tid, emp, id) ResolveTickets h⌫t, empi htid, empi htid, empi htid,em pi htid,em pi htid, empi hempi htid, descri htid,em pi Fig. 2. The control layer of a db-net for ticket management. In CreateTicket, ⌫t is a fresh input variable, and descr is an arbitrary input variable.
  • 97. Run! Idle Employees register (⌫t, emp, descr) CreateTicket Active tickets release (tid, emp) Stall assign (tid, emp) Awake Stalled tickets logData (tid, emp, id) ResolveTickets h⌫t, empi htid, empi htid, empi htid,em pi htid,em pi htid, empi hempi htid, descri htid,em pi Fig. 2. The control layer of a db-net for ticket management. In CreateTicket, ⌫t is a fresh input variable, and descr is an arbitrary input variable. Example 3. Figure 2 shows the control layer of a db-net B, using the persistence layer P defined in Example 1 and the data logic layer L defined in Example 2.2. The control layer realizes a simple ticket processing workflow, where tickets are created, Idle Employees register (⌫t, emp, descr) CreateTicket Active tickets logData (tid, emp, id) ResolveTickets h⌫t, empi htid, emp htid,em p htid, empi hempi htid, descri Fig. 2. The control layer of a db-net for ticket manage fresh input variable, and descr is an arbitrary input var Example 3. Figure 2 shows the control layer of a db layer P defined in Example 1 and the data logic layer L control layer realizes a simple ticket processing workfl Idle Employees register (⌫t, emp, descr) CreateTicket Active tickets release (tid, emp) Stall assign (tid, emp) Awake Stalled tickets logData (tid, emp, id) ResolveTickets h⌫t, empi htid, empi htid, empi htid,em pi htid,em pi htid, empi hempi htid, descri htid,em pi Fig. 2. The control layer of a db-net for ticket management. In CreateTicket, ⌫t is a fresh input variable, and descr is an arbitrary input variable. Emp Andy Marco TicketResp Log hAndyi hMarcoi
  • 98. Run! Idle Employees register (⌫t, emp, descr) CreateTicket Active tickets release (tid, emp) Stall assign (tid, emp) Awake Stalled tickets logData (tid, emp, id) ResolveTickets h⌫t, empi htid, empi htid, empi htid,em pi htid,em pi htid, empi hempi htid, descri htid,em pi Fig. 2. The control layer of a db-net for ticket management. In CreateTicket, ⌫t is a fresh input variable, and descr is an arbitrary input variable. Example 3. Figure 2 shows the control layer of a db-net B, using the persistence layer P defined in Example 1 and the data logic layer L defined in Example 2.2. The control layer realizes a simple ticket processing workflow, where tickets are created, Idle Employees register (⌫t, emp, descr) CreateTicket Active tickets logData (tid, emp, id) ResolveTickets h⌫t, empi htid, emp htid,em p htid, empi hempi htid, descri Fig. 2. The control layer of a db-net for ticket manage fresh input variable, and descr is an arbitrary input var Example 3. Figure 2 shows the control layer of a db layer P defined in Example 1 and the data logic layer L control layer realizes a simple ticket processing workfl Idle Employees register (⌫t, emp, descr) CreateTicket Active tickets release (tid, emp) Stall assign (tid, emp) Awake Stalled tickets logData (tid, emp, id) ResolveTickets h⌫t, empi htid, empi htid, empi htid,em pi htid,em pi htid, empi hempi htid, descri htid,em pi Fig. 2. The control layer of a db-net for ticket management. In CreateTicket, ⌫t is a fresh input variable, and descr is an arbitrary input variable. TicketResp Log hAndyi hMarcoi ⌫t = 1 emp = Andy descr = blah Emp Andy Marco
  • 99. Run! Idle Employees register (⌫t, emp, descr) CreateTicket Active tickets release (tid, emp) Stall assign (tid, emp) Awake Stalled tickets logData (tid, emp, id) ResolveTickets h⌫t, empi htid, empi htid, empi htid,em pi htid,em pi htid, empi hempi htid, descri htid,em pi Fig. 2. The control layer of a db-net for ticket management. In CreateTicket, ⌫t is a fresh input variable, and descr is an arbitrary input variable. Example 3. Figure 2 shows the control layer of a db-net B, using the persistence layer P defined in Example 1 and the data logic layer L defined in Example 2.2. The control layer realizes a simple ticket processing workflow, where tickets are created, Idle Employees register (⌫t, emp, descr) CreateTicket Active tickets logData (tid, emp, id) ResolveTickets h⌫t, empi htid, emp htid,em p htid, empi hempi htid, descri Fig. 2. The control layer of a db-net for ticket manage fresh input variable, and descr is an arbitrary input var Example 3. Figure 2 shows the control layer of a db layer P defined in Example 1 and the data logic layer L control layer realizes a simple ticket processing workfl Idle Employees register (⌫t, emp, descr) CreateTicket Active tickets release (tid, emp) Stall assign (tid, emp) Awake Stalled tickets logData (tid, emp, id) ResolveTickets h⌫t, empi htid, empi htid, empi htid,em pi htid,em pi htid, empi hempi htid, descri htid,em pi Fig. 2. The control layer of a db-net for ticket management. In CreateTicket, ⌫t is a fresh input variable, and descr is an arbitrary input variable. Ticket 1 blah Resp Andy 1 Log hMarcoi Emp Andy Marco h1, Andyi
  • 100. Run! Idle Employees register (⌫t, emp, descr) CreateTicket Active tickets release (tid, emp) Stall assign (tid, emp) Awake Stalled tickets logData (tid, emp, id) ResolveTickets h⌫t, empi htid, empi htid, empi htid,em pi htid,em pi htid, empi hempi htid, descri htid,em pi Fig. 2. The control layer of a db-net for ticket management. In CreateTicket, ⌫t is a fresh input variable, and descr is an arbitrary input variable. Example 3. Figure 2 shows the control layer of a db-net B, using the persistence layer P defined in Example 1 and the data logic layer L defined in Example 2.2. The control layer realizes a simple ticket processing workflow, where tickets are created, Idle Employees register (⌫t, emp, descr) CreateTicket Active tickets logData (tid, emp, id) ResolveTickets h⌫t, empi htid, emp htid,em p htid, empi hempi htid, descri Fig. 2. The control layer of a db-net for ticket manage fresh input variable, and descr is an arbitrary input var Example 3. Figure 2 shows the control layer of a db layer P defined in Example 1 and the data logic layer L control layer realizes a simple ticket processing workfl Idle Employees register (⌫t, emp, descr) CreateTicket Active tickets release (tid, emp) Stall assign (tid, emp) Awake Stalled tickets logData (tid, emp, id) ResolveTickets h⌫t, empi htid, empi htid, empi htid,em pi htid,em pi htid, empi hempi htid, descri htid,em pi Fig. 2. The control layer of a db-net for ticket management. In CreateTicket, ⌫t is a fresh input variable, and descr is an arbitrary input variable. Ticket 1 blah Resp Andy 1 Log hMarcoi Emp Andy Marco h1, Andyi tid = 1 emp = Andy
  • 101. Run! Idle Employees register (⌫t, emp, descr) CreateTicket Active tickets release (tid, emp) Stall assign (tid, emp) Awake Stalled tickets logData (tid, emp, id) ResolveTickets h⌫t, empi htid, empi htid, empi htid,em pi htid,em pi htid, empi hempi htid, descri htid,em pi Fig. 2. The control layer of a db-net for ticket management. In CreateTicket, ⌫t is a fresh input variable, and descr is an arbitrary input variable. Example 3. Figure 2 shows the control layer of a db-net B, using the persistence layer P defined in Example 1 and the data logic layer L defined in Example 2.2. The control layer realizes a simple ticket processing workflow, where tickets are created, Idle Employees register (⌫t, emp, descr) CreateTicket Active tickets logData (tid, emp, id) ResolveTickets h⌫t, empi htid, emp htid,em p htid, empi hempi htid, descri Fig. 2. The control layer of a db-net for ticket manage fresh input variable, and descr is an arbitrary input var Example 3. Figure 2 shows the control layer of a db layer P defined in Example 1 and the data logic layer L control layer realizes a simple ticket processing workfl Idle Employees register (⌫t, emp, descr) CreateTicket Active tickets release (tid, emp) Stall assign (tid, emp) Awake Stalled tickets logData (tid, emp, id) ResolveTickets h⌫t, empi htid, empi htid, empi htid,em pi htid,em pi htid, empi hempi htid, descri htid,em pi Fig. 2. The control layer of a db-net for ticket management. In CreateTicket, ⌫t is a fresh input variable, and descr is an arbitrary input variable. Resp Log hAndyi hMarcoi Emp Andy Marco Ticket 1 blah h1, blahi h1, Andyi
  • 102. Run! Idle Employees register (⌫t, emp, descr) CreateTicket Active tickets release (tid, emp) Stall assign (tid, emp) Awake Stalled tickets logData (tid, emp, id) ResolveTickets h⌫t, empi htid, empi htid, empi htid,em pi htid,em pi htid, empi hempi htid, descri htid,em pi Fig. 2. The control layer of a db-net for ticket management. In CreateTicket, ⌫t is a fresh input variable, and descr is an arbitrary input variable. Example 3. Figure 2 shows the control layer of a db-net B, using the persistence layer P defined in Example 1 and the data logic layer L defined in Example 2.2. The control layer realizes a simple ticket processing workflow, where tickets are created, Idle Employees register (⌫t, emp, descr) CreateTicket Active tickets logData (tid, emp, id) ResolveTickets h⌫t, empi htid, emp htid,em p htid, empi hempi htid, descri Fig. 2. The control layer of a db-net for ticket manage fresh input variable, and descr is an arbitrary input var Example 3. Figure 2 shows the control layer of a db layer P defined in Example 1 and the data logic layer L control layer realizes a simple ticket processing workfl Idle Employees register (⌫t, emp, descr) CreateTicket Active tickets release (tid, emp) Stall assign (tid, emp) Awake Stalled tickets logData (tid, emp, id) ResolveTickets h⌫t, empi htid, empi htid, empi htid,em pi htid,em pi htid, empi hempi htid, descri htid,em pi Fig. 2. The control layer of a db-net for ticket management. In CreateTicket, ⌫t is a fresh input variable, and descr is an arbitrary input variable. Resp Log hAndyi hMarcoi Emp Andy Marco Ticket 1 blah h1, blahi h1, Andyi ⌫t = 5 emp = Andy descr = blah
  • 103. Run! Idle Employees register (⌫t, emp, descr) CreateTicket Active tickets release (tid, emp) Stall assign (tid, emp) Awake Stalled tickets logData (tid, emp, id) ResolveTickets h⌫t, empi htid, empi htid, empi htid,em pi htid,em pi htid, empi hempi htid, descri htid,em pi Fig. 2. The control layer of a db-net for ticket management. In CreateTicket, ⌫t is a fresh input variable, and descr is an arbitrary input variable. Example 3. Figure 2 shows the control layer of a db-net B, using the persistence layer P defined in Example 1 and the data logic layer L defined in Example 2.2. The control layer realizes a simple ticket processing workflow, where tickets are created, Idle Employees register (⌫t, emp, descr) CreateTicket Active tickets logData (tid, emp, id) ResolveTickets h⌫t, empi htid, emp htid,em p htid, empi hempi htid, descri Fig. 2. The control layer of a db-net for ticket manage fresh input variable, and descr is an arbitrary input var Example 3. Figure 2 shows the control layer of a db layer P defined in Example 1 and the data logic layer L control layer realizes a simple ticket processing workfl Idle Employees register (⌫t, emp, descr) CreateTicket Active tickets release (tid, emp) Stall assign (tid, emp) Awake Stalled tickets logData (tid, emp, id) ResolveTickets h⌫t, empi htid, empi htid, empi htid,em pi htid,em pi htid, empi hempi htid, descri htid,em pi Fig. 2. The control layer of a db-net for ticket management. In CreateTicket, ⌫t is a fresh input variable, and descr is an arbitrary input variable. Log hMarcoi Emp Andy Marco Ticket 1 blah 2 blah h1, blahi h1, Andyi Resp Andy 2 h2, Andyi h2, blahi
  • 104. Run! Idle Employees register (⌫t, emp, descr) CreateTicket Active tickets release (tid, emp) Stall assign (tid, emp) Awake Stalled tickets logData (tid, emp, id) ResolveTickets h⌫t, empi htid, empi htid, empi htid,em pi htid,em pi htid, empi hempi htid, descri htid,em pi Fig. 2. The control layer of a db-net for ticket management. In CreateTicket, ⌫t is a fresh input variable, and descr is an arbitrary input variable. Example 3. Figure 2 shows the control layer of a db-net B, using the persistence layer P defined in Example 1 and the data logic layer L defined in Example 2.2. The control layer realizes a simple ticket processing workflow, where tickets are created, Idle Employees register (⌫t, emp, descr) CreateTicket Active tickets logData (tid, emp, id) ResolveTickets h⌫t, empi htid, emp htid,em p htid, empi hempi htid, descri Fig. 2. The control layer of a db-net for ticket manage fresh input variable, and descr is an arbitrary input var Example 3. Figure 2 shows the control layer of a db layer P defined in Example 1 and the data logic layer L control layer realizes a simple ticket processing workfl Idle Employees register (⌫t, emp, descr) CreateTicket Active tickets release (tid, emp) Stall assign (tid, emp) Awake Stalled tickets logData (tid, emp, id) ResolveTickets h⌫t, empi htid, empi htid, empi htid,em pi htid,em pi htid, empi hempi htid, descri htid,em pi Fig. 2. The control layer of a db-net for ticket management. In CreateTicket, ⌫t is a fresh input variable, and descr is an arbitrary input variable. Log hMarcoi Emp Andy Marco Ticket 1 blah 2 blah h1, Andyi Resp Andy 2 h2, Andyi tid = 1 emp = Andy h1, blahi h2, blahi
  • 105. Run? Idle Employees register (⌫t, emp, descr) CreateTicket Active tickets release (tid, emp) Stall assign (tid, emp) Awake Stalled tickets logData (tid, emp, id) ResolveTickets h⌫t, empi htid, empi htid, empi htid,em pi htid,em pi htid, empi hempi htid, descri htid,em pi Fig. 2. The control layer of a db-net for ticket management. In CreateTicket, ⌫t is a fresh input variable, and descr is an arbitrary input variable. Example 3. Figure 2 shows the control layer of a db-net B, using the persistence layer P defined in Example 1 and the data logic layer L defined in Example 2.2. The control layer realizes a simple ticket processing workflow, where tickets are created, Idle Employees register (⌫t, emp, descr) CreateTicket Active tickets logData (tid, emp, id) ResolveTickets h⌫t, empi htid, emp htid,em p htid, empi hempi htid, descri Fig. 2. The control layer of a db-net for ticket manage fresh input variable, and descr is an arbitrary input var Example 3. Figure 2 shows the control layer of a db layer P defined in Example 1 and the data logic layer L control layer realizes a simple ticket processing workfl Idle Employees register (⌫t, emp, descr) CreateTicket Active tickets release (tid, emp) Stall assign (tid, emp) Awake Stalled tickets logData (tid, emp, id) ResolveTickets h⌫t, empi htid, empi htid, empi htid,em pi htid,em pi htid, empi hempi htid, descri htid,em pi Fig. 2. The control layer of a db-net for ticket management. In CreateTicket, ⌫t is a fresh input variable, and descr is an arbitrary input variable. Log hMarcoi Emp Andy Marco Ticket 1 blah 2 blah h1, Andyi Resp Andy 2 Andy 1 h2, Andyi tid = 1 emp = Andy h1, blahi h2, blahi
  • 106. Rollback Flow Accounts for the production and routing of tokens when the application of a ground action fails • update ok: update committed on the DB, normal output flow used, rollback flow ignored • update violates some constraint: rollback on the DB, rollback flow used, normal output flow ignored The rollback flow can be used to model “undo” or “compensation” in the control layer when the persistence layer rejects an update
  • 107. Example: “Undo” Idle Employees register (⌫t, emp, descr) CreateTicket Active tickets release (tid, emp) Stall assign (tid, emp) Awake Stalled tickets logData (tid, emp, id) ResolveTickets h⌫t, empi htid, empi htid, empi htid,em pi htid,em pi htid, empi hempi htid, descri htid,em pi Fig. 2. The control layer of a db-net for ticket management. In CreateTicket, ⌫t is a fresh input variable, and descr is an arbitrary input variable. Example 3. Figure 2 shows the control layer of a db-net B, using the persistence layer P defined in Example 1 and the data logic layer L defined in Example 2.2. The control layer realizes a simple ticket processing workflow, where tickets are created,
  • 108. Run! Idle Employees register (⌫t, emp, descr) CreateTicket Active tickets release (tid, emp) Stall assign (tid, emp) Awake Stalled tickets logData (tid, emp, id) ResolveTickets h⌫t, empi htid, empi htid, empi htid,em pi htid,em pi htid, empi hempi htid, descri htid,em pi Fig. 2. The control layer of a db-net for ticket management. In CreateTicket, ⌫t is a fresh input variable, and descr is an arbitrary input variable. Example 3. Figure 2 shows the control layer of a db-net B, using the persistence layer P defined in Example 1 and the data logic layer L defined in Example 2.2. The control layer realizes a simple ticket processing workflow, where tickets are created, Idle Employees register (⌫t, emp, descr) CreateTicket Active tickets logData (tid, emp, id) ResolveTickets h⌫t, empi htid, emp htid,em p htid, empi hempi htid, descri Fig. 2. The control layer of a db-net for ticket manage fresh input variable, and descr is an arbitrary input var Example 3. Figure 2 shows the control layer of a db layer P defined in Example 1 and the data logic layer L control layer realizes a simple ticket processing workfl Idle Employees register (⌫t, emp, descr) CreateTicket Active tickets release (tid, emp) Stall assign (tid, emp) Awake Stalled tickets logData (tid, emp, id) ResolveTickets h⌫t, empi htid, empi htid, empi htid,em pi htid,em pi htid, empi hempi htid, descri htid,em pi Fig. 2. The control layer of a db-net for ticket management. In CreateTicket, ⌫t is a fresh input variable, and descr is an arbitrary input variable. Log hMarcoi Emp Andy Marco Ticket 1 blah 2 blah h1, Andyi Resp Andy 2 Andy 1 h2, Andyi tid = 1 emp = Andy h1, blahi h2, blahi
  • 109. Run! Idle Employees register (⌫t, emp, descr) CreateTicket Active tickets release (tid, emp) Stall assign (tid, emp) Awake Stalled tickets logData (tid, emp, id) ResolveTickets h⌫t, empi htid, empi htid, empi htid,em pi htid,em pi htid, empi hempi htid, descri htid,em pi Fig. 2. The control layer of a db-net for ticket management. In CreateTicket, ⌫t is a fresh input variable, and descr is an arbitrary input variable. Example 3. Figure 2 shows the control layer of a db-net B, using the persistence layer P defined in Example 1 and the data logic layer L defined in Example 2.2. The control layer realizes a simple ticket processing workflow, where tickets are created, Idle Employees register (⌫t, emp, descr) CreateTicket Active tickets logData (tid, emp, id) ResolveTickets h⌫t, empi htid, emp htid,em p htid, empi hempi htid, descri Fig. 2. The control layer of a db-net for ticket manage fresh input variable, and descr is an arbitrary input var Example 3. Figure 2 shows the control layer of a db layer P defined in Example 1 and the data logic layer L control layer realizes a simple ticket processing workfl Log hMarcoi Emp Andy Marco Ticket 1 blah 2 blah h1, Andyi Resp Andy 2 h2, Andyi tid = 1 emp = Andy h1, blahi h2, blahi