An SMT-Based Discovery Algorithm for C-Nets

摘要

Recently, Causal nets have been proposed as a suitable model for process discovery, due to their declarative semantics and the great expressiveness they possess. In this paper we propose an algorithm to discover a causal net from a set of traces. It is based on encoding the problem as a Satisfiability Modulo Theories (SMT) formula, and uses a binary search strategy to optimize the derived model. The method has been implemented in a prototype tool that interacts with an SMT solver. The experimental results obtained witness the capability of the approach to discover complex behavior in limited time.