The document discusses the formalization and reasoning support for data-aware business processes, emphasizing the integration of data and processes as essential components of organizations. It explores the dichotomy between data and process management perspectives, which leads to challenges in collaboration and tool development. The paper outlines goals for enhancing the formalization of data-aware processes, including verification and monitoring techniques that account for both data and dynamic processes.

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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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, . . .
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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. 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. 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.
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19. Concrete Example
Dynamic web applications.
• Current data fields, text, highlights.
Include status attributes.
• Actions buttons, links.
• Input params writable fields.
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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. 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
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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. 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. 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. 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. 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. 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. 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. 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. 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.
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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. 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!
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33. Genericity
Consider action (π, τ).
A1 asktell
A2
asktell
hif
h is a bijection that is the identity over C.
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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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. Operational Support with MoBuCon EC [Montali et al.,ACM TIST2014]
Marco Montali Data-Aware Business Processes Aug 2013 30 / 34

48. Trace Analysis with SCIFF Checker [Chesani et al., BPI2008]
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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.
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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. 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
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