Causal Behavioural Profiles - Efficient Computation, Applications, and Evaluation
Web of Science
AuthorWeidlich, M; Polyvyanyy, A; Mendling, J; Weske, M
Source TitleFundamenta Informaticae
University of Melbourne Author/sPolyvyanyy, Artem
AffiliationComputing and Information Systems
Document TypeJournal Article
CitationsWeidlich, M., Polyvyanyy, A., Mendling, J. & Weske, M. (2011). Causal Behavioural Profiles - Efficient Computation, Applications, and Evaluation. Fundamenta Informaticae, 113 (3-4), pp.399-435. https://doi.org/10.3233/FI-2011-614.
Access StatusOpen Access
Analysis of behavioural consistency is an important aspect of software engineering. In process and service management, consistency verification of behavioural models has manifold applications. For instance, a business process model used as system specification and a corresponding workflow model used as implementation have to be consistent. Another example would be the analysis to what degree a process log of executed business operations is consistent with the corresponding normative process model. Typically, existing notions of behaviour equivalence, such as bisimulation and trace equivalence, are applied as consistency notions. Still, these notions are exponential in computation and yield a Boolean result. In many cases, however, a quantification of behavioural deviation is needed along with concepts to isolate the source of deviation. In this article, we propose causal behavioural profiles as the basis for a consistency notion. These profiles capture essential behavioural information, such as order, exclusiveness, and causality between pairs of activities of a process model. Consistency based on these profiles is weaker than trace equivalence, but can be computed efficiently for a broad class of models. In this article, we introduce techniques for the computation of causal behavioural profiles using structural decomposition techniques for sound free-choice workflow systems if unstructured net fragments are acyclic or can be traced back to S- or T-nets. We also elaborate on the findings of applying our technique to three industry model collections.
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