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Model Consistency Checking

Model Consistency Checking. Yong Zhao E-mail: yz300@uow.edu.au. Outline. Introduction Consistency constraint Approaches Conclusion & future work. Abstract.

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Model Consistency Checking

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  1. Model Consistency Checking Yong Zhao E-mail: yz300@uow.edu.au

  2. Outline • Introduction • Consistency constraint • Approaches • Conclusion & future work

  3. Abstract • Process portfolio often encodes multiple ways of doing same thing. Models may be described at varying levels of detail, and varying levels of completeness. Thus some models in a process portfolio might be refinements of other models in a process portfolio. Some models might describe a fragment of another model. These can cause a range of management problems. We have defined techniques to determine whether a given set of BPMN models is consistent. Then we extend these notions to define a looser, but more practical notion of graded model consistency that can involve measuring the degree of similarity between models that violate an absolute test for consistency.

  4. Introduction • Why need consistency check? • Model play central role in software development • E-Business, enterprises collaborate across organizational boundaries

  5. Introduction Consistency as a basic quality attribute • Consistency is crucial • A model must be consistency before it is transformed into other forms • Consistency: • Between different views • Between models at different levels of abstraction

  6. Introduction • Models (descriptions) focus on views corresponding to • system parts • class, component, subsystem • 􀂄 aspects • data, function, distribution, security • 􀂄 user views • clerk, customer, system administrator • 􀂄 …

  7. Introduction

  8. Introduction • Ensuring consistency is very difficult • Semantics of the model • Complexity due to multiple views and multiple levels

  9. Introduction • The aim of consistency check is to provide a partial but effective solution to model consistency problem. • Define a set of constraints that can detect a large number of common errors in models • Design algorithms that can automatically check if a model satisfies the consistency constraints

  10. Consistency constraints • What are consistent constraints? • Restriction on the uses of diagrammatic notions, variable and names, types and symbols in a modeling language to reduce the possibility of inconsistency • Example • The same identifier that occurs at different places must refer to the same entity • An entity should be referred to by the same identifier if it occurs at different diagrams

  11. Types of Consistency constraints • Intra-model consistency: within one type of model • Intra-diagram: within one diagram • Inter-diagram: between different diagrams of the same model • Inter-model consistency: • Between different types of models, hence also different diagrams

  12. Types of Consistency constraints • Horizontal consistency: • Between models/diagrams of the same level of abstraction • Vertical consistency: between models/diagrams at different levels of abstraction • Local: between two levels • Global: with respect to the overall structure

  13. Related work • Marc Ehrig, Agnes Koschmider and Andreas Oberweis propose an approach of measuring the similarity between business process models semantically modeled with the Web Ontology Language (OWL) • Yun Lin(2004) examines the conceptual modeling processes by separating concept concerns in problem frame. • Marc Ehrig and Agnes Koschmider(2007) measuring the similarity between business process models semantically modeled with the Web Ontology Language. • Li Chen provided a method to quantitatively measure the distance and similarity between two process models based on the efforts for model transformation.

  14. Approach • Assumptions • Name conflict have been solved • Abstraction conflict have been solved • Input a pair of process models and process, output a similarity measure which is between 0 and 1

  15. Approach 1 • Node • < ID, nodetype, owner > • Edge • <<u, v>, edgetype > • d • Stand for diagraphs • | d | total number nodes and edges in d

  16. Approach 1 • Parse models from XML • Encode the process models into diagraphs di and dj • Computer total number of nodes plus edge on which two diagraphs thus obtained agree, denoted by | intersect (di, dj) |

  17. Approach 1 • Similarity measure • min(| intersect (di, dj) |/| di |, | intersect (di, dj) |/| dj |,) • Threshold • Tunable parameter

  18. Approach 1 • Model i and model j is consistent iff • The sub-graphs within dI and dj defined by the nodes common to dI and dj are isomorphic • For each incoming edge connecting a common node to a node that does not belong to the intersection in one diagraph, there does not exist a corresponding incoming edge connecting the same common node in the other.

  19. Approach 2 • Combination measurement • Syntactic Similarity Measure • ed: edit distance • |c| length of c

  20. Approach 2 • Linguistic Similarity Measure • ƞ(c) phrasing of the given ontological concept instance c. • Let S = ƞ(c1) ∩ ƞ(c2) • Let max(|ƞ(c1)|, |ƞ(c2)|) be the maximum of the cardinalities of the two sets ƞ(c1) and ƞ(c2)

  21. Approach 2 • Structural Similarity Measure • simki(c1i,c2j)denotes the specific similarity measure used for the context elements of c1 and c2 which we multiply with individual weights • This measure returns a similarity degree of 1.0 if the syntactical and/or linguistic similarity for the context elements equals 1.0.

  22. Approach 2 • Combined Similarity Measure

  23. Example

  24. Example • For approach 1 • Min(7/21,7/17)≈0.33 • For approach 2 • simSPBM ≈0.32

  25. Example • Approach 1 only structural measure, no semantic measure • While approach 2 only nodes • Could not say which is more better

  26. Conclusion • My work • Implementation of algorithm 1 in Eclipse • Detail evaluation of the tool using industry-scale cases.

  27. Future work • Improve the algorithm • Re-design and modification of the toolkit based on evaluating results.

  28. Reference • Lin, Y 2004, Applying Problem Frames to Modeling ‘Abstraction’ Concepts. In proceeding of 1st International Workshop on Advances and Applications of Problem Frames in International Conference on Software Engineering (ICSE 2004). Edinburgh, Scotland, May 2004. • Krogstie, J, Veres, C, & Sindre, G 2005, “Interoperability Through Integrating Semantic Web Technology, Web Services, and Workflow Modeling”, In Proc. 1st International Conf. on Interoperability of Enterprise Software and Applications, Feb.2005. • Lin, Y, & Strasunskas, D 2005, “Ontology-based Semantic Annotation of Process Templates for Reuse”, In Proc. 10th Intl. Workshop on Exploring Modeling Methods in System Analysis and Design (EMMSAD’05), Porto, Portugal, 2005. • Marc,E, Agnes,K, & Andreas,O 2007, Measuring similarity between semantic business process models, Proceedings of the fourth Asia-Pacific conference on Comceptual modelling, p.71-80, January 30-February 02, 2007, Ballarat, Australia • Li, C, Reichert, M, & Wombacher, A 2008, On Measuring Process Model Similarity based on High-level Change Operations. In: 27th International Conference on Conceptual Modeling (ER'08), October 2008, Barcelona, Spain. • ……

  29. End Thank you!

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