CoCoMoT: Conformance Checking of Multi-Perspective Processes via SMT (Extended Version)

• URL: http://arxiv.org/abs/2103.10507v1
• Date: Thu, 18 Mar 2021 20:22:50 GMT
• Title: CoCoMoT: Conformance Checking of Multi-Perspective Processes via SMT (Extended Version)
• Authors: Paolo Felli and Alessandro Gianola and Marco Montali and Andrey Rivkin and Sarah Winkler
• Abstract summary: We introduce the CoCoMoT (Computing Conformance Modulo Theories) framework. First, we show how SAT-based encodings studied in the pure control-flow setting can be lifted to our data-aware case. Second, we introduce a novel preprocessing technique based on a notion of property-preserving clustering.
• Score: 62.96267257163426
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Conformance checking is a key process mining task for comparing the expected behavior captured in a process model and the actual behavior recorded in a log. While this problem has been extensively studied for pure control-flow processes, conformance checking with multi-perspective processes is still at its infancy. In this paper, we attack this challenging problem by considering processes that combine the data and control-flow dimensions. In particular, we adopt data Petri nets (DPNs) as the underlying reference formalism, and show how solid, well-established automated reasoning techniques can be effectively employed for computing conformance metrics and data-aware alignments. We do so by introducing the CoCoMoT (Computing Conformance Modulo Theories) framework, with a fourfold contribution. First, we show how SAT-based encodings studied in the pure control-flow setting can be lifted to our data-aware case, using SMT as the underlying formal and algorithmic framework. Second, we introduce a novel preprocessing technique based on a notion of property-preserving clustering, to speed up the computation of conformance checking outputs. Third, we provide a proof-of-concept implementation that uses a state-of-the-art SMT solver and report on preliminary experiments. Finally, we discuss how CoCoMoT directly lends itself to a number of further tasks, like multi- and anti-alignments, log analysis by clustering, and model repair.

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