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Data-Aware Business Processes
Formalization and Reasoning Support
Marco Montali
KRDB Research Centre for Knowledge and Dat...
Data and Processes
The information assets of an organization are constituted by:
• data, and
• processes, that determine h...
Data and Processes
The information assets of an organization are constituted by:
• data, and
• processes, that determine h...
Data and Processes
The information assets of an organization are constituted by:
• data, and
• processes, that determine h...
Data Modeling
Example: (UML class diagram)
Supplier ManufacturingProcurement/Supplier
Sales
Customer PO Line Item
Work Ord...
Data Modeling
Example: (UML class diagram)
Supplier ManufacturingProcurement/Supplier
Sales
Customer PO Line Item
Work Ord...
Process Modeling
Example: (BPMN diagram)
• Focus is on control flow of activities that realize the business goals.
• High l...
Process Modeling
Example: (BPMN diagram)
• Focus is on control flow of activities that realize the business goals.
• High l...
Data and Processes
Survey by Forrester investigates which of the two aspects should be given
priority from the point of vi...
Data and Processes
Survey by Forrester investigates which of the two aspects should be given
priority from the point of vi...
Data and Processes
Survey by Forrester investigates which of the two aspects should be given
priority from the point of vi...
Data and Processes
The need for overcoming this dichotomy is recognized by the BPM
community as well [Reichert 2012]:
“Pro...
Data and Processes
The need for overcoming this dichotomy is recognized by the BPM
community as well [Reichert 2012]:
“Pro...
Data and Processes
Lack of a holistic view of data+processes makes it difficult to:
• Reconstruct and aggregate the relevant...
Our goals
Formalization of Data-Aware Business Processes
Lay the foundations for processes dealing with a full-fledged data...
Business Process Verification
In BPM, process verification is considered the second most influential topic
in the last decade...
Business Process Verification
In BPM, process verification is considered the second most influential topic
in the last decade...
Our Goals Refined
Formalization
Devise a general framework for DL-based Dynamic Systems:
• Static component: full-fledged De...
Concrete Example
Dynamic web applications.
• Current data fields, text, highlights.
Include status attributes.
• Actions bu...
Concrete Example
Dynamic web applications.
• Current data fields, text, highlights.
Include status attributes.
• Actions bu...
Description Logic-Based Dynamic Systems [Calvanese et al.,RR2013]
Parametric wrt DL and progression mechanism.
DLDS
Tuple ...
Description Logic-Based Dynamic Systems [Calvanese et al.,RR2013]
Parametric wrt DL and progression mechanism.
DLDS
Tuple ...
Description Logic-Based Dynamic Systems [Calvanese et al.,RR2013]
Parametric wrt DL and progression mechanism.
DLDS
Tuple ...
Description Logic-Based Dynamic Systems [Calvanese et al.,RR2013]
Parametric wrt DL and progression mechanism.
DLDS
Tuple ...
Description Logic-Based Dynamic Systems [Calvanese et al.,RR2013]
Parametric wrt DL and progression mechanism.
DLDS
Tuple ...
Example
Registration of credit card number(s)
m<Travel Info>
Traveler(john)
ask
⇡
12345
T
traveler
current state
<Travel I...
Execution Semantics
Infinite-state transition system Υ.
• States: KBs.
• (Labeled) transitions: actions+param assignments.
...
Verification: Beyond State Explosion
What is a finite-state control process with possibly unbounded data?
Marco Montali Data...
Verification: Beyond State Explosion
What is a finite-state control process with possibly unbounded data?
Turing machine
Hal...
Design Space
Requirements for temporal/dynamic properties
• To capture data: first-order queries.
• To capture dynamics: te...
Towards Decidability: Controlling the Shape of the System
Genericity
Actions can only distinguish a bounded number of indi...
Genericity: Intuition
Travel payment
register
credit card
cc number
...
pay
bank status
...
...
...
status = "OK"
status =...
Genericity
Consider action (π, τ).
A1 asktell
A2
asktell
hif
h is a bijection that is the identity over C.
Marco Montali D...
Genericity
Consider action (π, τ).
m2
m1
A1 asktell
⇡
d1 d2 ... dN
P1 P2 PN
v1 v2 ... vN
P1 P2 PN
...
...
h
A2
asktell
h
⇡...
Genericity
Consider action (π, τ).
m2
m1
A1 asktell
⇡
d1 d2 ... dN
⌧P1 P2 PN
v1 v2 ... vN
P1 P2 PN
...
...
h
⌧
h
A'1 askte...
Example
Registration of credit card number(s)
m…
Traveler(john)
⇡
12345
T
CC number
m
…
Traveler(bob)
⇡
6574
T
CC number
j...
Example
Registration of credit card number(s)
m…
Traveler(john)
⇡
12345
T
…
Traveler(john)
cc(john,12345)
T
⌧CC number
m
…...
Towards Decidability: Controlling the Shape of the System
Genericity
Actions can only distinguish a bounded number of indi...
Summary of Results [Calvanese et al.,RR2013][Bagheri Hariri et al.,PODS2013]
Theorem
Verification of first-order µ-calculus ...
Data Quality and BPM
Consider a business process and the following tasks:
• computation of KPIs and statistics;
• auditing...
Real-World Processes
Recurrent situation: process executed in the real-world, storing partial
information later on.
Exampl...
Quality-Aware Transition Systems [Razniewski et al., BPM 2013]
Language-independent study on how to verify completeness pr...
Reasoning Tasks
Design-time verification
Check query completeness in a given state of the process.
Run-time verification
Che...
Declarative Process Modelling with Declare
Declare: constraint-based language for declarative process modeling.
• Originat...
Extending Declare
Missing features in the basic language:
• Metric time constraints
added in [Montali et al.,ACM TWEB2010]...
Process Monitoring with Declare++
[Montali et al.,SAC2013]
Level
Level < 50 glucose test.Patient = eat.O
Patient
(0,180)
g...
Operational Support with MoBuCon EC [Montali et al.,ACM TIST2014]
Marco Montali Data-Aware Business Processes Aug 2013 30 ...
Trace Analysis with SCIFF Checker [Chesani et al., BPI2008]
Marco Montali Data-Aware Business Processes Aug 2013 31 / 34
Towards Mining of Business Rules [Maggi et al., BPM 2013]
Discovery Declare constraints with case data and data-aware cond...
Conclusion
• Need to consider the interplay between data and processes.
• Need to reassess the corresponding reasoning sup...
Thanks
Involved in this research
• Diego Calvanese
• Federico Chesani
• Giuseppe De Giacomo
• Alin Deutsch
• Marlon Dumas
...
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Dagstuhl 2013 - Montali - Data-Aware Business Processes - Formalization and Reasoning Support

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Presentation on "Data-Aware Business Processes - Formalization and Reasoning Support" at the Dagstuhl Seminar on Verifiably Secure Process-Aware Information Systems.

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Dagstuhl 2013 - Montali - Data-Aware Business Processes - Formalization and Reasoning Support

  1. 1. Data-Aware Business Processes Formalization and Reasoning Support Marco Montali KRDB Research Centre for Knowledge and Data Free University of Bozen-Bolzano Dagstuhl, August 2013 Marco Montali Data-Aware Business Processes Aug 2013 1 / 34
  2. 2. Data and Processes The information assets of an organization are constituted by: • data, and • processes, that determine how data changes and evolves over time. Marco Montali Data-Aware Business Processes Aug 2013 2 / 34
  3. 3. Data and Processes The information assets of an organization are constituted by: • data, and • processes, that determine how data changes and evolves over time. Conceptual Modeling Both aspects are modelled conceptually, but: • Using different modeling tools • By different teams with different competences • Connection between the two is NOT modelled conceptually Marco Montali Data-Aware Business Processes Aug 2013 2 / 34
  4. 4. Data and Processes The information assets of an organization are constituted by: • data, and • processes, that determine how data changes and evolves over time. Conceptual Modeling Both aspects are modelled conceptually, but: • Using different modeling tools • By different teams with different competences • Connection between the two is NOT modelled conceptually Consequence Full reasoning support, e.g., for verification taking into account both process and data, is not possible! Marco Montali Data-Aware Business Processes Aug 2013 2 / 34
  5. 5. Data Modeling Example: (UML class diagram) Supplier ManufacturingProcurement/Supplier Sales Customer PO Line Item Work OrderMaterial PO * * spawns 0..1 Material • Focus is on entities, relations, and static constraints that are relevant for the domain of interest. • ER-diagrams, UML Class Diagrams, DBs, Data Integration, . . . • Ontologies, OBDA, OBDI, Automated Reasoning Marco Montali Data-Aware Business Processes Aug 2013 3 / 34
  6. 6. Data Modeling Example: (UML class diagram) Supplier ManufacturingProcurement/Supplier Sales Customer PO Line Item Work OrderMaterial PO * * spawns 0..1 Material • Focus is on entities, relations, and static constraints that are relevant for the domain of interest. • ER-diagrams, UML Class Diagrams, DBs, Data Integration, . . . • Ontologies, OBDA, OBDI, Automated Reasoning But how do data evolve? Marco Montali Data-Aware Business Processes Aug 2013 3 / 34
  7. 7. Process Modeling Example: (BPMN diagram) • Focus is on control flow of activities that realize the business goals. • High level (Conceptual) models, BPMN diagrams, UML Activity Diagrams. • Execution, monitoring, verification (some hacking needed). Marco Montali Data-Aware Business Processes Aug 2013 4 / 34
  8. 8. Process Modeling Example: (BPMN diagram) • Focus is on control flow of activities that realize the business goals. • High level (Conceptual) models, BPMN diagrams, UML Activity Diagrams. • Execution, monitoring, verification (some hacking needed). But how are data manipulated? Marco Montali Data-Aware Business Processes Aug 2013 4 / 34
  9. 9. Data and Processes Survey by Forrester investigates which of the two aspects should be given priority from the point of view of IT management [Forrester Report 2009] Marco Montali Data-Aware Business Processes Aug 2013 5 / 34
  10. 10. Data and Processes Survey by Forrester investigates which of the two aspects should be given priority from the point of view of IT management [Forrester Report 2009] • Business process management professionals: view data as subsidiary to processes, and neglect importance of data quality. • Data management experts: consider data as the driver of the organizational processes and are concerned about data quality only. Marco Montali Data-Aware Business Processes Aug 2013 5 / 34
  11. 11. Data and Processes Survey by Forrester investigates which of the two aspects should be given priority from the point of view of IT management [Forrester Report 2009] • Business process management professionals: view data as subsidiary to processes, and neglect importance of data quality. • Data management experts: consider data as the driver of the organizational processes and are concerned about data quality only. Dichotomy in the relative perception of importance has a negative impact: • Little collaboration between the teams running the master data management initiatives, and managing the business processes. Forrester: 83% . . . no interaction at all. • Little attention on the side of tool vendors to address the combined requirements: Data management tools consider only the low-level processes directly affecting the data in the tools. Business process modeling suites see data as procedural attachment. Marco Montali Data-Aware Business Processes Aug 2013 5 / 34
  12. 12. Data and Processes The need for overcoming this dichotomy is recognized by the BPM community as well [Reichert 2012]: “Process and data are just two sides of the same coin” Marco Montali Data-Aware Business Processes Aug 2013 6 / 34
  13. 13. Data and Processes The need for overcoming this dichotomy is recognized by the BPM community as well [Reichert 2012]: “Process and data are just two sides of the same coin” Two key areas where explicit representation of data in business processes is important [Meyer et al. 2011]: 1 Modeling the core assets of an organization. Data is crucial for the execution of business processes that create value. Hence the business processes need to access the data, and this should be accounted for explicitly. 2 Business process controlling. KPIs and business goals are defined in terms of data. To evaluate and control them, the data objects relevant for the activities contributing to the goals need to be identified. Marco Montali Data-Aware Business Processes Aug 2013 6 / 34
  14. 14. Data and Processes Lack of a holistic view of data+processes makes it difficult to: • Reconstruct and aggregate the relevant information: part of the data is in the DBs, part of the data is hidden in the process execution engine. • Decide how and where to model the domain relevant business rules: At the DB level? Which DB? How to import the process data? (Also) in the business model? How to import data from the DBs? Example: build-to-order DataProcess Supplier ManufacturingProcurement/Supplier Sales Customer PO Line Item Work OrderMaterial PO * * spawns 0..1 Determine cancelation penalty Notify penalty Material Process Engine Process State Business rules For each work order W For each material PO M in W if M has been shipped add returnCost(M) to penalty Marco Montali Data-Aware Business Processes Aug 2013 7 / 34
  15. 15. Our goals Formalization of Data-Aware Business Processes Lay the foundations for processes dealing with a full-fledged data layer: • relational database with constraints (complete information); • conceptual model/ontology (incomplete information); • both. Reasoning support along the entire process lifecycle Develop techniques for: • (design time) verification and synthesis; • (run-time) operational support (monitoring); • (a-posteriori) analysis and mining. Grounding of the approach Focus on concrete languages/settings like: business artifacts + adaptive case management (GSM, CMMN, . . . ), healthcare processes, dynamic web apps, . . . Marco Montali Data-Aware Business Processes Aug 2013 8 / 34
  16. 16. Business Process Verification In BPM, process verification is considered the second most influential topic in the last decade [van der Aalst 2012]. However: • Data is abstracted away (fully or partially). • Emphasis is on the control-flow dimension: sophisticated techniques for absence of deadlocks, boundedness, soundness, or domain-dependent properties expressed in LTL, CTL, µ-calculus. Marco Montali Data-Aware Business Processes Aug 2013 9 / 34
  17. 17. Business Process Verification In BPM, process verification is considered the second most influential topic in the last decade [van der Aalst 2012]. However: • Data is abstracted away (fully or partially). • Emphasis is on the control-flow dimension: sophisticated techniques for absence of deadlocks, boundedness, soundness, or domain-dependent properties expressed in LTL, CTL, µ-calculus. Basic assumption: control-flow is captured by a transition system: • labels on transitions represent the process tasks/activities • concurrency is represented by interleaving • transition system usually not represented explicitly, but typically is implicitly “folded” into a Petri net Marco Montali Data-Aware Business Processes Aug 2013 9 / 34
  18. 18. Our Goals Refined Formalization Devise a general framework for DL-based Dynamic Systems: • Static component: full-fledged Description Logic KB (or a DB with constraints). • Dynamic component: parametric actions. Progress the system by evolving the ABox -TBox is fixed. Parameters: inject new values from the external world. The transition system is not propositional anymore: states are KBs / DBs. Verification Check whether the system satisfies temporal/dynamic properties. • Analyzing all possible executions. Adversarial Synthesis Action separation+alternation: system actions vs environment actions. System goal: force the execution to satisfy a desired property. Marco Montali Data-Aware Business Processes Aug 2013 10 / 34
  19. 19. Concrete Example Dynamic web applications. • Current data fields, text, highlights. Include status attributes. • Actions buttons, links. • Input params writable fields. Marco Montali Data-Aware Business Processes Aug 2013 11 / 34
  20. 20. Concrete Example Dynamic web applications. • Current data fields, text, highlights. Include status attributes. • Actions buttons, links. • Input params writable fields. Their number depends on the current data! Marco Montali Data-Aware Business Processes Aug 2013 11 / 34
  21. 21. Description Logic-Based Dynamic Systems [Calvanese et al.,RR2013] Parametric wrt DL and progression mechanism. DLDS Tuple (T, A0, Γ), where • (T, A0): initial DL KB during system progression: A0 evolves. • Γ: parametric actions (π, τ) that evolve the system. Given ABox A: π: determines action params depending on A. τ: given A+param values is undef. or gives new T-consistent ABox. A asktell T current state Marco Montali Data-Aware Business Processes Aug 2013 12 / 34
  22. 22. Description Logic-Based Dynamic Systems [Calvanese et al.,RR2013] Parametric wrt DL and progression mechanism. DLDS Tuple (T, A0, Γ), where • (T, A0): initial DL KB during system progression: A0 evolves. • Γ: parametric actions (π, τ) that evolve the system. Given ABox A: π: determines action params depending on A. τ: given A+param values is undef. or gives new T-consistent ABox. A asktell ⇡ param selection T current state P1 P2 PN ... Marco Montali Data-Aware Business Processes Aug 2013 12 / 34
  23. 23. Description Logic-Based Dynamic Systems [Calvanese et al.,RR2013] Parametric wrt DL and progression mechanism. DLDS Tuple (T, A0, Γ), where • (T, A0): initial DL KB during system progression: A0 evolves. • Γ: parametric actions (π, τ) that evolve the system. Given ABox A: π: determines action params depending on A. τ: given A+param values is undef. or gives new T-consistent ABox. m A asktell ⇡ param selection d1 d2 ... dN param assignment T external actors/services current state P1 P2 PN ... Note: param values taken from countably infinite set. Marco Montali Data-Aware Business Processes Aug 2013 12 / 34
  24. 24. Description Logic-Based Dynamic Systems [Calvanese et al.,RR2013] Parametric wrt DL and progression mechanism. DLDS Tuple (T, A0, Γ), where • (T, A0): initial DL KB during system progression: A0 evolves. • Γ: parametric actions (π, τ) that evolve the system. Given ABox A: π: determines action params depending on A. τ: given A+param values is undef. or gives new T-consistent ABox. m A asktell ⇡ param selection d1 d2 ... dN param assignment effect function T external actors/services current state Anew asktell T next state ⌧P1 P2 PN ... Note: adom(Anew) ⊆ adom(A) ∪ im(m). Marco Montali Data-Aware Business Processes Aug 2013 12 / 34
  25. 25. Description Logic-Based Dynamic Systems [Calvanese et al.,RR2013] Parametric wrt DL and progression mechanism. DLDS Tuple (T, A0, Γ), where • (T, A0): initial DL KB during system progression: A0 evolves. • Γ: parametric actions (π, τ) that evolve the system. Given ABox A: π: determines action params depending on A. τ: given A+param values is undef. or gives new T-consistent ABox. A T Anew T (labeled) transition ((⇡, ⌧), m) Marco Montali Data-Aware Business Processes Aug 2013 12 / 34
  26. 26. Example Registration of credit card number(s) m<Travel Info> Traveler(john) ask ⇡ 12345 T traveler current state <Travel Info> Traveler(john) cc(john,12345) asktell T next state ⌧CC number tell Marco Montali Data-Aware Business Processes Aug 2013 13 / 34
  27. 27. Execution Semantics Infinite-state transition system Υ. • States: KBs. • (Labeled) transitions: actions+param assignments. Construction: 1 Start from A0 2 Apply each action + param assignments in all possible ways 3 Recur over newly generated states. ... . . . ... . . . ... . . . A1 T A2 T A0 T A2 T . . . Sources of infinity: • Infinite branching - infinitely many param assignments. • Infinite runs - usage of values obtained from action steps. • Unbounded Aboxes - accumulation of such values. Marco Montali Data-Aware Business Processes Aug 2013 14 / 34
  28. 28. Verification: Beyond State Explosion What is a finite-state control process with possibly unbounded data? Marco Montali Data-Aware Business Processes Aug 2013 15 / 34
  29. 29. Verification: Beyond State Explosion What is a finite-state control process with possibly unbounded data? Turing machine Halt curState == qf Transition done ... status attributes curState cellscurCell curCell = curCell.next; Head moved if curCell.next == null newCell = createCell(); newCell.value = "_"; curCell.next = newCell; newCell.prev = curCell; newCell.next = null; Tape extended if curCell.next != null curCell = createCell(); curCell.value = "_"; curState = q0;Initialized if curCell == null MovedR ... curCell.value = vR1'; curState = qR1'; if curState = qR1 && curCell.value = vR1 R1 state updated ... curCell.value = vRk'; curState = qRk'; if curState = qRk && curCell.value = vRk Rk state updated ... value prev next Transition stage State update stages Init stage Right shift stage (left transitions) (Left shift stage) . . .. . . Turing machine in GSM [ICSOC 2013] Verification of propositional CTL ∩ LTL reachability properties is undecidable for data-aware processes. Marco Montali Data-Aware Business Processes Aug 2013 15 / 34
  30. 30. Design Space Requirements for temporal/dynamic properties • To capture data: first-order queries. • To capture dynamics: temporal modalities. Both branching- and linear-time. • To capture evolution of data: quantification across states. Variants of first-order µ-calculus: the most expressive temporal logic. Example For every traveler, there will be an execution leading to a state where some travel is booked by him/her. How to reconcile these requirements with the undecidability result? • Delimiting the way actions manipulate data. • Controlling the quantification across states. Marco Montali Data-Aware Business Processes Aug 2013 16 / 34
  31. 31. Towards Decidability: Controlling the Shape of the System Genericity Actions can only distinguish a bounded number of individuals C (adom(A0) ⊆ C): • those: could affect the behavior of the actions. • for the others: invariance of behavior (modulo renaming). Marco Montali Data-Aware Business Processes Aug 2013 17 / 34
  32. 32. Genericity: Intuition Travel payment register credit card cc number ... pay bank status ... ... ... status = "OK" status = "ERR" else + For analyzing the system (considering all possible executions): • The actual credit card number does not matter. • What matters is the outcome of the payment. The process behavior: • Distinguishes the bank status. • Does not “see” the actual cc number only how it relates with the other objects! Marco Montali Data-Aware Business Processes Aug 2013 18 / 34
  33. 33. Genericity Consider action (π, τ). A1 asktell A2 asktell hif h is a bijection that is the identity over C. Marco Montali Data-Aware Business Processes Aug 2013 19 / 34
  34. 34. Genericity Consider action (π, τ). m2 m1 A1 asktell ⇡ d1 d2 ... dN P1 P2 PN v1 v2 ... vN P1 P2 PN ... ... h A2 asktell h ⇡ andif h is a bijection that is the identity over C. Marco Montali Data-Aware Business Processes Aug 2013 19 / 34
  35. 35. Genericity Consider action (π, τ). m2 m1 A1 asktell ⇡ d1 d2 ... dN ⌧P1 P2 PN v1 v2 ... vN P1 P2 PN ... ... h ⌧ h A'1 asktell A'2 asktell then A2 asktell h ⇡ andif h is a bijection that is the identity over C. Marco Montali Data-Aware Business Processes Aug 2013 19 / 34
  36. 36. Example Registration of credit card number(s) m… Traveler(john) ⇡ 12345 T CC number m … Traveler(bob) ⇡ 6574 T CC number john ⇿ bob 12345 ⇿ 6574 Marco Montali Data-Aware Business Processes Aug 2013 20 / 34
  37. 37. Example Registration of credit card number(s) m… Traveler(john) ⇡ 12345 T … Traveler(john) cc(john,12345) T ⌧CC number m … Traveler(bob) ⇡ 6574 T … Traveler(bob) cc(bob,6574) T ⌧CC number john ⇿ bob 12345 ⇿ 6574 Marco Montali Data-Aware Business Processes Aug 2013 20 / 34
  38. 38. Towards Decidability: Controlling the Shape of the System Genericity Actions can only distinguish a bounded number of individuals C (adom(A0) ⊆ C): • those: could affect the behavior of the actions. • for the others: invariance of behavior (modulo renaming). Genericity alone does not suffice. Run boundedness Each run of the DLDS accumulates only a bounded number of objects. No bound on the overall number of objects: the DLDS is still infinite-state, due to infinite branching induced by parameter choice. State boundedness Each state of the DLDS contains only bounded number of objects. Relaxation of run-boundedness: unboundedly many objects along a run, provided that they are not accumulated in the same state. Marco Montali Data-Aware Business Processes Aug 2013 21 / 34
  39. 39. Summary of Results [Calvanese et al.,RR2013][Bagheri Hariri et al.,PODS2013] Theorem Verification of first-order µ-calculus with active domain quantification is decidable for generic, run-bounded DLDSs. Undecidability is encountered again for generic, state-bounded DLDSs. Theorem Verification of first-order µ-calculus with quantification limited to persistent objects is decidable for generic, state-bounded DLDSs. In both cases: decidability obtained via construction of a faithful (sound and complete) finite-state abstraction of the system. • Reduction to conventional model checking techniques. Run-boundedness and state-boundedness are semantic conditions, undecidable to check. • In [PODS2013,JAIR2013] a concrete specification language for tasks is given, and sufficient syntactic conditions are introduced. Marco Montali Data-Aware Business Processes Aug 2013 22 / 34
  40. 40. Data Quality and BPM Consider a business process and the following tasks: • computation of KPIs and statistics; • auditing; • compliance/conformance checking. These tasks: • Need to consider control-flow (events) + data. • Depend on the reliability of such information. Completeness: presence of all relevant data to answer a given query. These issues have been extensively investigated in the field of data quality, but neglecting the connection with the system dynamics (processes). • Work by Nutt and Razniewski on query completeness starting from user statements about table completeness. Question How to extract completeness guarantees from process models? Marco Montali Data-Aware Business Processes Aug 2013 23 / 34
  41. 41. Real-World Processes Recurrent situation: process executed in the real-world, storing partial information later on. Example Administrative processes: legal events are valid as soon as they are recorded in a (signed) piece of paper. Parents Submit enrolment applications Schools School Administration Decide about enrolment requests Enrolment applications Record accepted enrolments Enrolment deadline School information system Generate statistics and assign resources Can I currently obtain a reliable answer to: SELECT school, COUNT(*) as pupils FROM pupil GROUP BY school Incompleteness due to misalignment between the reality and the information system. Marco Montali Data-Aware Business Processes Aug 2013 24 / 34
  42. 42. Quality-Aware Transition Systems [Razniewski et al., BPM 2013] Language-independent study on how to verify completeness properties over processes. Idea: the process operates over two databases: • (ideal) real-world DB; • (concrete) information system DB (contained in the real-world DB). The process is made “quality-aware” through task annotation. • Real-world effect: new data are acquired by the process in the real world. pupilrw (n, 1, s) requestrw (n, s) • Copy effect: real-world data are stored in the information system. pupilrw (n, c, s), c > 3 → pupilis (n, c, s) Marco Montali Data-Aware Business Processes Aug 2013 25 / 34
  43. 43. Reasoning Tasks Design-time verification Check query completeness in a given state of the process. Run-time verification Check query completeness given a partial trace of a process execution. Dimension analysis When a query is not found to be complete, determine the relevant data that are complete for the answer. All tasks are reduced to checking forms of query containment. Marco Montali Data-Aware Business Processes Aug 2013 26 / 34
  44. 44. Declarative Process Modelling with Declare Declare: constraint-based language for declarative process modeling. • Originated from ConDec [Pesic and van der Aalst, BPM WS2006] and DecSerFlow [Pesic and van der Aalst, WS-FM2006][Montali et al.,ACM TWEB 2010]. commit order 0..1 refuse order confirm order refuse shipment confirm shipment Marco Montali Data-Aware Business Processes Aug 2013 27 / 34
  45. 45. Extending Declare Missing features in the basic language: • Metric time constraints added in [Montali et al.,ACM TWEB2010], studied in [Montali,Book2010],[Maggi and Westergaard, CAISE2012]. • Non-atomic activities with a lifecycle added in [Montali, Book2010][Montali et al.,ACM TIST2014]. • Data/resource-aware conditions added in [Montali et al.,SAC2013]. Note Such extensions challenge static verification, but are helpful for operational support and process mining. Marco Montali Data-Aware Business Processes Aug 2013 28 / 34
  46. 46. Process Monitoring with Declare++ [Montali et al.,SAC2013] Level Level < 50 glucose test.Patient = eat.O Patient (0,180) glucose test assume sugar Marco Montali Data-Aware Business Processes Aug 2013 29 / 34
  47. 47. Operational Support with MoBuCon EC [Montali et al.,ACM TIST2014] Marco Montali Data-Aware Business Processes Aug 2013 30 / 34
  48. 48. Trace Analysis with SCIFF Checker [Chesani et al., BPI2008] Marco Montali Data-Aware Business Processes Aug 2013 31 / 34
  49. 49. Towards Mining of Business Rules [Maggi et al., BPM 2013] Discovery Declare constraints with case data and data-aware conditions. • Improves coverage, while reducing the number of constraints. Marco Montali Data-Aware Business Processes Aug 2013 32 / 34
  50. 50. Conclusion • Need to consider the interplay between data and processes. • Need to reassess the corresponding reasoning support techniques along the business process lifecycle. • Things become extremely difficult, but there is hope!!! • Need for cross-fertilization between different areas: BPM, database theory, knowledge representation, AI. See, e.g., [Calvanese et al., PODS2013] • Case studies! • What about the third BPM pillar, namely the organisational structure? Marco Montali Data-Aware Business Processes Aug 2013 33 / 34
  51. 51. Thanks Involved in this research • Diego Calvanese • Federico Chesani • Giuseppe De Giacomo • Alin Deutsch • Marlon Dumas • Domenico Lembo • Fabrizio Maggi • Paola Mello • Fabio Patrizi • Babak Bagheri Hariri (PhD) • Riccardo De Masellis (PhD) • Paolo Felli (PhD) • Ario Santoso (PhD) • Dmitry Solomakhin (PhD) EU Project ACSI • Rick Hull • Lior Limonad • Alessio Lomuscio • Wil van der Aalst Marco Montali Data-Aware Business Processes Aug 2013 34 / 34

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