Learning regular sets from queries and counterexamples
Information and Computation
Mining Process Models from Workflow Logs
EDBT '98 Proceedings of the 6th International Conference on Extending Database Technology: Advances in Database Technology
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: a survey of issues and approaches
Data & Knowledge Engineering
Workflow Management: Models, Methods, and Systems
Workflow Management: Models, Methods, and Systems
Testing Software Design Modeled by Finite-State Machines
IEEE Transactions on Software Engineering
Construction of Process Models from Example Runs
Transactions on Petri Nets and Other Models of Concurrency II
Process mining framework for software processes
ICSP'07 Proceedings of the 2007 international conference on Software process
ProM 4.0: comprehensive support for real process analysis
ICATPN'07 Proceedings of the 28th international conference on Applications and theory of Petri nets and other models of concurrency
Process mining based on regions of languages
BPM'07 Proceedings of the 5th international conference on Business process management
Learning Communicating Automata from MSCs
IEEE Transactions on Software Engineering
Process mining and petri net synthesis
BPM'06 Proceedings of the 2006 international conference on Business Process Management Workshops
libalf: the automata learning framework
CAV'10 Proceedings of the 22nd international conference on Computer Aided Verification
Analysis of the Petri net model of parallel manufacturing processes with shared resources
Information Sciences: an International Journal
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Workflow mining is the task of automatically producing a workflow model from a set of event logs recording sequences of workflow events; each sequence corresponds to a use case or workflow instance. Formal approaches to workflow mining assume that the event log is complete (contains enough information to infer the workflow) which is often not the case. We present a learning approach that relaxes this assumption: if the event log is incomplete, our learning algorithm automatically derives queries about the executability of some event sequences. If a teacher answers these queries, the algorithm is guaranteed to terminate with a correct model. We provide matching upper and lower bounds on the number of queries required by the algorithm, and report on the application of an implementation to some examples.