Validation of User Intentions in Process Models

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Validation of User Intentions in Process Models

  1. 1. Web Science & Technologies University of Koblenz ▪ Landau, Germany Validation of User Intentions in Process ModelsGerd Gröner, Mohsen Asadi, Bardia Mohabatti, Dragan Gasevic, Fernando Silva Parreiras and Marko Boskovic
  2. 2. What is the goal of a particular process?Goal that should be achieved  several goals  subgoals  dependencies between goals ?Process Model: ➔ Operational representation of activities to achieve a certain goalWeST Gerd Gröner CAiSE 2012 groener@uni-koblenz.de 2
  3. 3. Example: Traveling to Gdansk for CAiSE quick journey ✔comfortable journey ↯ ✔ cheaplate arrival journeygoals and dependencies activities and dependenciesamong goals between them ➔ Dependencies / relationships might be contradicting!WeST Gerd Gröner CAiSE 2012 groener@uni-koblenz.de 3
  4. 4. Problems and Questions How are goals represented? Which kinds of quick dependencies journey are covered in a process model? comfortable journey late arrival cheap journey How is the influence of a mapped goal on an activity? How to map / align between goals and activities? (What is the meaning?)WeST Gerd Gröner CAiSE 2012 groener@uni-koblenz.de 4
  5. 5. Idea 1. Extract and represent relationships of both models logical view: quick ➔ similar journey relationships between elements in both comfortable journey models late arrival cheap journey 2. Explicitly represent mappings between goals and activities 3. Classify inconsistencies between mapped goals and activities4. Formalization for validation and recognition of inconsistenciesWeST Gerd Gröner CAiSE 2012 groener@uni-koblenz.de 5
  6. 6. Outline1. Modeling dimensions i. Goal models ii. Process models2. Realization inconsistencies3. Modeling principles4. Validation5. Discussion and conclusionWeST Gerd Gröner CAiSE 2012 groener@uni-koblenz.de 6
  7. 7. Requirements representationRequirements: goals, functions and constraints of a system→ Representation: means for understanding user intentions andhow they are related to each other G5 G OR OR Goal models: + • Graph with G1 G4 AND  intentional elements (hard AND goals, soft goals, tasks) G2 G3  links (contributions)  decompositions (AND, IOR, XOR) ➔ requirements of a system-to-beWeST Gerd Gröner CAiSE 2012 groener@uni-koblenz.de 7
  8. 8. Intentions in Process Models Requirement perspective: Process model: • Goals (user intentions) • Control flow perspective • Relationships (constraints, - activities dependencies) - ordering through different constructors preferred payment AND AND no additional 30 days term fee of credit mapping ≈ realization of a goalWeST Gerd Gröner CAiSE 2012 groener@uni-koblenz.de 8
  9. 9. … Problem Requirement perspective: Control flow perspective: preferred payment AND AND no additional 30 days term fee of credit mapping relations not necessarily coincide, they might even be contradictingWeST Gerd Gröner CAiSE 2012 groener@uni-koblenz.de 9
  10. 10. Outline1. Modeling dimensions i. Goal models ii. Process models2. Realization inconsistencies3. Modeling principles4. Validation5. Discussion and conclusionWeST Gerd Gröner CAiSE 2012 groener@uni-koblenz.de 10
  11. 11. Realization InconsistenciesIntentional relations (IR) Control flow relations /over intentional elements workflow relations WF overG1, …,Gm ∈ G activities A1, …,An ∈ A WF over A1, …,An ∈ A  and IR over G1, …,Gm ∈ G, with target goal G ∈ G and activities A1, …,An are realizations of G1, …,Gm  A strong inconsistency between WF and IR occurs if there is no execution combination of activities that leads to the fulfillment of the target goal G.  A potential inconsistency between WF and IR occurs if some execution combinations of activities lead to the fulfillment of G and some do not lead to the fulfillment of G.WeST Gerd Gröner CAiSE 2012 groener@uni-koblenz.de 11
  12. 12. Realization Inconsistencies – ExampleStrong inconsistency: preferred payment AND AND no additional 30 days term fee of creditPotential inconsistency: preferred payment AND AND 30 days term little of credit payment effortWeST Gerd Gröner CAiSE 2012 groener@uni-koblenz.de 12
  13. 13. Correspondences Intentional relationsControl flow relations AND IOR XOR ● - ● +AND-AND parallel split - synchronization ✔ ✔ ↯ ✔ ↯AND-OR parallel split – multi merge ✔ ✔ ↯ ✔ ↯AND-Disc parallel split – discriminator ✔ ✔ ↯ ✔ ↯AND-XOR parallel split – simple merge ✔ ✔ ↯ ✔ ↯IOR-IOR multi choice - multi merge ± ✔ ± ± ±IOR-Disc multi choice - discriminator ± ✔ ± ± ±IOR-XOR multi choice – simple merge ± ✔ ± ± ±XOR-XOR exclusive – simple merge ↯ ✔ ✔ ± ✔Sequence ✔ ✔ ↯ ✔ ↯ ↯ strong inconsistency, ± potential inconsistency, ✔ no inconsistency WeST Gerd Gröner CAiSE 2012 groener@uni-koblenz.de 13
  14. 14. Outline1. Modeling dimensions i. Goal models ii. User intentions for process models2. Realization inconsistencies3. Modeling principles4. Validation5. Discussion and conclusionWeST Gerd Gröner CAiSE 2012 groener@uni-koblenz.de 14
  15. 15. Description Logics (DLs)(1) DL Knowledge base (KB) Concepts C C(x) Property (role) R R(x,y) Subclass C⊑D ∀ x (C(x) → D(x)) Negation ¬C ¬ C(x) Union C⊔D C(x) ∨ D(x) Intersection C⊓D C(x) ∧ D(x) Existential Quantification ∃ P.C ∃ y (P(x,y)∧C(y))(2) Inference service: Subsumption: C⊑D? if KB ⊨ C ⊑ DWeST Gerd Gröner CAiSE 2012 groener@uni-koblenz.de 15
  16. 16. Towards a common knowledge base (atomic) concepts: - goals - activities A1 G1 A2 G2 A3 G3 complex concept expressions - intentional relations of Gi - control flow relations of Aj connect concepts (atomic concepts) of mapped entitiesWeST Gerd Gröner CAiSE 2012 groener@uni-koblenz.de 16
  17. 17. Complex concept expressions – for IR Preferred payment (PP) RelCT ≡ ∃ relates . NF ⊓ AND AND ∃ relates. CT no add. 30 credit fee (NF) term (CT) DTC XOR XOR RelCRC ≡ ( ∃ relates . CRC ⊔ ∃ relates . CCR ) ⊓ CCR CRC ¬ (∃ relates . CTC ⊓ ∃ relates . CCT) Minimize Risk (MR) + RelAP ≡ ( ∃ relates . MR ) • Apply Process (AP)WeST Gerd Gröner CAiSE 2012 groener@uni-koblenz.de 17
  18. 18. Complex concept expressions – for WF RelFD ≡ ∃ relates . FD ⊓ ∃ relates. AD RelCP ≡ ( ∃ relates . CP ⊔ ∃ relates . CCP ) ⊓ ¬ (∃ relates . CP ⊓ ∃ relates . CCP)WeST Gerd Gröner CAiSE 2012 groener@uni-koblenz.de 18
  19. 19. Outline1. Modeling dimensions i. Goal models ii. User intentions for process models2. Realization inconsistencies3. Modeling principles4. Validation5. Discussion and conclusionWeST Gerd Gröner CAiSE 2012 groener@uni-koblenz.de 19
  20. 20. How to detect inconsistencies? G1 A1 G2 A2 A3 G4 G3 atomic WF by IR by concept complex complex equivalence concept concept ➔ complex concept expressions: logical formulas ➔ comparison of concept expressionsWeST Gerd Gröner CAiSE 2012 groener@uni-koblenz.de 20
  21. 21. Validation – slightly abstracted Logical point of view: G1 M A1 RelG RelA Mi Validation principle: • compare RelG and RelA if there is a mapping between G and A RelG RelA ? - coincide / equivalent ? - contradicting? - no influence? • both models are correct on their own • only mapped elements need to be consideredWeST Gerd Gröner CAiSE 2012 groener@uni-koblenz.de 21
  22. 22. Relation comparison Strong inconsistency  ...no execution combination fulfills the target goal … ➔ KB: no common instance: RelG ⊓ RelA ⊑ ⊥ Potential inconsistency  … there are some execution combinations that do not lead to the fulfillment of G... ➔ an execution combination not necessarily fulfills G ➔ KB: entailment RelA ⊑ RelG does not hold: ¬(RelA ⊑ RelG) Otherwise  Relations coincide WeST Gerd Gröner CAiSE 2012 groener@uni-koblenz.de 22
  23. 23. Relation comparison – concept level RelG RelA ? compare RelG and RelA ✔ RelA ⊑ RelG ± ¬(RelA ⊑ RelG) ↯ RelG ⊓ RelA ⊑ ⊥ ✔ relations ± depends on ↯ relations contradict coincide particular execution ➔ detect inconsistency between both models ➔ identify the source (i.e., activity and goal) of an inconsistencyWeST Gerd Gröner CAiSE 2012 groener@uni-koblenz.de 23
  24. 24. Resolve inconsistency RelG RelA inconsistency detected change intentional relations change control flow relations change mapping ➔ individual assessmentWeST Gerd Gröner CAiSE 2012 groener@uni-koblenz.de 24
  25. 25. Conclusion 2 views / perspectives goals / intentions activities and their execution DTC XOR mapping XOR CCR CRC (realization of goals) intentional control flow relationships relationships ➔ Problem: goals and activities depend on other goals and activities ➔ Mapping imposes to an activity also relationship from its corresponding goalWeST Gerd Gröner CAiSE 2012 groener@uni-koblenz.de 25
  26. 26. Conclusion (2)Approach1) Specifying realization inconsistencies2) Formalizing relationships of both models3) Detection of inconsistencies of mapped goals and activitiesContribution of DLs Detection of inconsistencies  Potential inconsistency  Strong inconsistency Pinpointing of sources for inconsistenciesFuture Work: focus on behavioral constraints (semi-) automatic derivation of process modelsWeST Gerd Gröner CAiSE 2012 groener@uni-koblenz.de 26
  27. 27. Thank you!WeST Gerd Gröner CAiSE 2012 groener@uni-koblenz.de 27

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