Despite the abundance of analysis techniques to discover control-flow errors in workflow designs, there is hardly any support for data-flow verification. Most techniques simply abstract from data, while data dependencies can be the source of all kinds of errors. This paper focuses on the discovery of data-flow errors in workflows. We present an analysis approach that uses so-called "anti-patterns" expressed in terms of a temporal logic. Typical errors include accessing a data element that is not yet available or updating a data element while it may be read in a parallel branch. Since the anti-patterns are expressed in terms of temporal logic, the well-known, stable, adaptable, and effective modelchecking techniques can be used to discover data-flow errors. Moreover, our approach enables a seamless integration of control flow and data-flow verification.

No abstract

We consider the relational characterisation of branching bisimilarity with explicit divergence. We prove that it is an equivalence and that it coincides with the original definition of branching bisimilarity with explicit divergence in terms of coloured traces. We also establish a correspondence with several variants of an action-based modal logic with until-and divergence modalities.

No abstract

Educational process mining (EPM) aims at (i) constructing complete and compact educational process models that are able to reproduce all observed behavior (process model discovery), (ii) checking whether the modeled behavior (either pre-authored or discovered from data) matches the observed behavior (conformance checking), and (iii) projecting information extracted from the logs onto the model, to make the tacit knowledge explicit and facilitate better understanding of the process (process model extension). In this paper we propose a new domain-driven framework for EPM which assumes that a set of pattern templates can be predefined to focus the mining in a desired way and make it more effective and efficient. We illustrate the ideas behind our approach with examples of academic curricular modeling, mining, and conformance checking, using the student database of our department.

No abstract

No abstract

A parallel composition is defined for Markov reward chains with stochastic discontinuity, and with fast and silent transitions. In this setting, compositionality with respect to the relevant aggregation preorders is established. For Markov reward chains with fast transitions the preorders are τ -lumping and τ -reduction. Discontinuous Markov reward chains are 'limits' of Markov reward chains with fast transitions, and have related notions of lumping and reduction. Markov reward chains with silent transitions are equivalence classes of Markov reward chains with fast transitions and come equipped with the lifted preorders τ ∼ -lumping and τ ∼ -reduction. In total, six compositionality results are presented. Additionally, the parallel operators involved are related by a continuity result.