Polynomial Algorithms for the Synthesis of Bounded Nets
TAPSOFT '95 Proceedings of the 6th International Joint Conference CAAP/FASE on Theory and Practice of Software Development
Workflow Mining: Discovering Process Models from Event Logs
IEEE Transactions on Knowledge and Data Engineering
Process Discovery Using Integer Linear Programming
PETRI NETS '08 Proceedings of the 29th international conference on Applications and Theory of Petri Nets
Beaver: Engineering an Efficient SMT Solver for Bit-Vector Arithmetic
CAV '09 Proceedings of the 21st International Conference on Computer Aided Verification
A decision procedure for bit-vectors and arrays
CAV'07 Proceedings of the 19th international conference on Computer aided verification
Process mining based on regions of languages
BPM'07 Proceedings of the 5th international conference on Business process management
A fresh look at precision in process conformance
BPM'10 Proceedings of the 8th international conference on Business process management
Process Mining: Discovery, Conformance and Enhancement of Business Processes
Process Mining: Discovery, Conformance and Enhancement of Business Processes
Causal nets: a modeling language tailored towards process discovery
CONCUR'11 Proceedings of the 22nd international conference on Concurrency theory
Conformance Checking Using Cost-Based Fitness Analysis
EDOC '11 Proceedings of the 2011 IEEE 15th International Enterprise Distributed Object Computing Conference
ICATPN'05 Proceedings of the 26th international conference on Applications and Theory of Petri Nets
Amending C-net discovery algorithms
Proceedings of the 28th Annual ACM Symposium on Applied Computing
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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.