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Causal Behavioural Profiles - Efficient Computation, Applications and Evaluation

Weidlich M., Mendling J., Polyvyanyy A., Weske M.

Fundamenta Informaticae

2011

Vol. 113, nr 3/4

399-435

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.

An Empirical Study of QBF Encodings: from Treewidth Estimation to Useful Preprocessing

Pulina L., Tacchella A.

Fundamenta Informaticae

2010

Vol. 102, nr 3/4

391-427

From an empirical point of view, the hardness of quantified Boolean formulas (QBFs), can be characterized by the (in)ability of current state-of-the-art QBF solvers to decide about the truth of formulas given limited computational resources. In this paper, we start from the problem of computing empirical hardness markers, i.e., features that can discriminate between hard and easy QBFs, and we end up showing that such markers can be useful to improve our understanding of QBF preprocessors. In particular, considering the connection between classes of tractable QBFs and the treewidth of associated graphs, we show that (an approximation of) treewidth is indeed a marker of empirical hardness and it is the only parameter which succeeds consistently in being so, even considering several other purely syntactic candidates which have been successfully employed to characterize QBFs in other contexts. We also show that treewidth approximations can be useful to describe the effect of QBF preprocessors, in that some QBF solvers benefit from a preprocessing phase when it reduces the treewidth of their input. Our experiments suggest that structural simplifications reducing treewidth are a potential enabler for the solution of hard QBF encodings.