1. Model Eco-systems Decision Systems Lab University of Wollongong

2. DecisionSystemsLab U of Wollongong Related references Ghose, Koliadis and Cheung. Rapid process discovery. ER-2007 Ghose, Koliadis and Cheung. Rapid process discovery. ER-2007 Ghose and Koliadis. Auditing business process compliance. ICSOC-2007 Ghose and Koliadis. Auditing business process compliance. ICSOC-2007 Koliadis and Ghose. Conistency in process inter-operation. SCC-2007 Koliadis and Ghose. Conistency in process inter-operation. SCC-2007

3. DecisionSystemsLab U of WollongongOutline The challenges of model management The challenges of model management The eco-systems metaphor The eco-systems metaphor The model life-cycle The model life-cycle The key technical challenges The key technical challenges Consistency equilibria Consistency equilibria Completeness equilibria Completeness equilibria

4. DecisionSystemsLab U of Wollongong The challenges of model management Large numbers of models Large numbers of models Large numbers of stakeholders Large numbers of stakeholders Diversity of modeling notations Diversity of modeling notations Models specified at varying levels of abstraction Models specified at varying levels of abstraction Distributed asynchronous model updates Distributed asynchronous model updates Changes in a given model impact several others Changes in a given model impact several others Collections of models are often inconsistent Collections of models are often inconsistent Collections of models are often incomplete Collections of models are often incomplete

5. DecisionSystemsLab U of Wollongong Why eco-systems (1/3) The (biological) eco-systems metaphor provides useful conceptual (and formal) tools to understand and instrument model management systems The (biological) eco-systems metaphor provides useful conceptual (and formal) tools to understand and instrument model management systems A biologically-inspired account of the model life-cycle: A biologically-inspired account of the model life-cycle: –Models are created (or discovered) –Models co-exist/co-evolve/inter-operate with other models during their lifetimes –Existing models lead to the creation of new models –Models are discarded/superannuated.

6. DecisionSystemsLab U of Wollongong Why eco-systems (2/3) Like biological eco-systems, models in a model eco-system undergo constant change Like biological eco-systems, models in a model eco-system undergo constant change –Requirements models change frequently because of »changing stakeholder perceptions, »evolving needs and »changing usage contexts. –Design models, process models, goal models, rule models and others also change for these (or similar) reasons Like biological eco-systems, perturbations in a model eco-system propagate across models, driven by the need to maintain a variety of inter-model constraints. Like biological eco-systems, perturbations in a model eco-system propagate across models, driven by the need to maintain a variety of inter-model constraints.

7. DecisionSystemsLab U of Wollongong Why eco-systems (3/3) Like biological eco-systems, model eco- systems are characterized by competing forces, such as: Like biological eco-systems, model eco- systems are characterized by competing forces, such as: –a pair of inconsistent models driving a specification in competing directions, or –the competing pulls of alternative ways to completing a specification Like biological eco-systems, model eco- systems settle into equilibria after being perturbed Like biological eco-systems, model eco- systems settle into equilibria after being perturbed

8. DecisionSystemsLab U of Wollongong More on equilibria An equilibrium is a “steady state”, where the competing forces within a system balance each other out An equilibrium is a “steady state”, where the competing forces within a system balance each other out Changes to the system perturb these equilibria, but the system eventually settles into a new equilibrium that accommodates these changes Changes to the system perturb these equilibria, but the system eventually settles into a new equilibrium that accommodates these changes Common concept in: Common concept in: –Physics (physical equilibria) –Chemistry –Economics (market equilibria) –Decision theory (Pareto-equilibria etc..)

9. DecisionSystemsLab U of Wollongong Inter-model constraints What is a minimal way of characterizing the constraints a given model imposes on others that co- exist with it? What is a minimal way of characterizing the constraints a given model imposes on others that co- exist with it? –Consistency –Completeness Formally, these are complementary notions Formally, these are complementary notions –Inconsistency resolved via removal –Incompleteness resolved via addition

10. DecisionSystemsLab U of Wollongong The hard questions How do we tell whether a set of models is consistent? How do we tell whether a set of models is consistent? How do we tell whether a set of models is complete? How do we tell whether a set of models is complete? How do we detect similarity between a pair of models? How do we detect similarity between a pair of models? How do we establish model co-reference (distinct models that describe the same real-world artefact)? How do we establish model co-reference (distinct models that describe the same real-world artefact)? How do we detect model inclusion? How do we detect model inclusion? How do we determine model entailment? How do we determine model entailment? What does it mean to minimally change a model or set of models? What does it mean to minimally change a model or set of models?

11. DecisionSystemsLab U of Wollongong Dealing with abstraction heterogeneity Models in an eco-system might be specified at varying levels of abstraction Models in an eco-system might be specified at varying levels of abstraction Comparisons (consistency/completeness) possible only with models specified at the same level of abstraction Comparisons (consistency/completeness) possible only with models specified at the same level of abstraction Current solution: Use enterprise ontologies to map more abstract models to more refined models Current solution: Use enterprise ontologies to map more abstract models to more refined models

12. DecisionSystemsLab U of Wollongong Model consistency Informally, a set of models is consistent if it can be realized Informally, a set of models is consistent if it can be realized Semantic: The existence of a specificand in the semantic domain Semantic: The existence of a specificand in the semantic domain Syntactic: The satisfaction of a set of consistency rules Syntactic: The satisfaction of a set of consistency rules Also, hybrid notions Also, hybrid notions

13. DecisionSystemsLab U of Wollongong Checking consistency Alternative (classes of) techniques: Mapping models to a (common) semantic domain Mapping models to a (common) semantic domain Semantic markup of models Semantic markup of models Metamodel-based approaches Metamodel-based approaches Rule-based approaches Rule-based approaches Hybrids Hybrids

14. DecisionSystemsLab U of Wollongong Consistency in model eco-systems Two types: Two types: –Intra-notation consistency –Inter-notation consistency

15. DecisionSystemsLab U of Wollongong Example: Intra-notation inconsistency (1/4)

16. DecisionSystemsLab U of Wollongong Example: Intra-notation inconsistency (2/4)

17. DecisionSystemsLab U of Wollongong Example: Intra-notation inconsistency (3/4) Define semantic correspondences between concepts Define semantic correspondences between concepts

18. DecisionSystemsLab U of Wollongong Example: Intra-notation inconsistency (4/4) How do we determine whether these models are inconsistent? How do we determine whether these models are inconsistent? –Use a graph encoding of BPMN models »activities, events and gateways become nodes »message and control flow links become edges –Use consistency rules specific to this encoding »Isomorphism of sub-graphs (in graph encoding of proto-models) determined by node intersections. »No dangling pointers

19. DecisionSystemsLab U of Wollongong Model inclusion Notions of equilibria (that we will discuss next) require reference to an evaluation of model inclusion Notions of equilibria (that we will discuss next) require reference to an evaluation of model inclusion Informally, m1 is included in m2 if m1 can be consistently extended to obtain m2 Informally, m1 is included in m2 if m1 can be consistently extended to obtain m2 Structural encodings permit sub-graph inclusion as a basis for model inclusion Structural encodings permit sub-graph inclusion as a basis for model inclusion Closely related to the notion of model entailment Closely related to the notion of model entailment

20. DecisionSystemsLab U of Wollongong Consistency equilibrium Resolution of consistency-perturbation of an equilibrium involves identifying maximal “subsets” (referring to a notion of model inclusion) of the eco-system that is consistent Resolution of consistency-perturbation of an equilibrium involves identifying maximal “subsets” (referring to a notion of model inclusion) of the eco-system that is consistent

21. DecisionSystemsLab U of Wollongong Minimal sources of inconsistency Forms basis for computing restored consistency equilibria by focusing analyst attention on specific portions of models in the eco-system that, if edited, would restore consistency

22. DecisionSystemsLab U of Wollongong Assessing completeness Informally: Completeness is an attribute of a model that “says all the things it needs to say” Informally: Completeness is an attribute of a model that “says all the things it needs to say” Formally: A formal theory is complete with respect to a background language if the theory entails either p or  p for every proposition p in the background language Formally: A formal theory is complete with respect to a background language if the theory entails either p or  p for every proposition p in the background language Completeness of a model in an eco-system can be assessed either via: Completeness of a model in an eco-system can be assessed either via: –Reference to a background ontology –Reference to other models in the eco-system Types of assessment: Types of assessment: –Intra-notation –Inter-notation

23. DecisionSystemsLab U of Wollongong Intra-notation completeness Intra-BPMN completeness rules Intra-BPMN completeness rules

24. DecisionSystemsLab U of Wollongong Inter-notation completeness Identify correspondences between meta- model elements of the 2 notations. If element e1 in (the meta-model of) notation n1 is related to element e2 in notation n2, then every instance of e1 in an n1 model requires that there is a corresponding instance of e2 in a (co-existing) n2 model Identify correspondences between meta- model elements of the 2 notations. If element e1 in (the meta-model of) notation n1 is related to element e2 in notation n2, then every instance of e1 in an n1 model requires that there is a corresponding instance of e2 in a (co-existing) n2 model Example: If a UML sequence diagram refers to a “customer” object, then a “customer” object must exist in the corresponding class diagram Example: If a UML sequence diagram refers to a “customer” object, then a “customer” object must exist in the corresponding class diagram

25. DecisionSystemsLab U of Wollongong Completeness Equilibrium