Discovering Petri Nets From Event Logs

As information systems are becoming more and more intertwined with the operational processes they support, multitudes of events are recorded by today’s information systems. The goal of process mining is to use such event data to extract process related information, e.g., to automatically discover a process model by observing events recorded by some system or to check the conformance of a given model by comparing it with reality. In this paper, the authors focus on process discovery, i.e., extracting a process model from an event log.

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Technische Universitat Munchen
Big Data