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Within process mining research, one of the most important fields of study is process discovery, which can be defined as the extraction of control-flow models from audit trails or information system event logs. The evaluation of discovered process models is an essential but difficult task for any process discovery analysis. With this paper, we propose a(More)
Process mining encompasses the research area which is concerned with knowledge discovery from event logs. One common process mining task focuses on conformance checking, comparing discovered or designed process models with actual real-life behavior as captured in event logs in order to assess the “goodness” of the process model. This paper(More)
The research area of business process mining has vastly matured in recent years. Its main focus centers around the extraction and analysis of process models from event logs. A strong emphasis lies on the automatic discovery of models for which numerous algorithms have been proposed already. So far, most discovery algorithms were limited to the derivation of(More)
Process discovery is the learning task that entails the construction of process models from event logs of information systems. Typically, these event logs are large data sets that contain the process executions by registering what activity has taken place at a certain moment in time. By far the most arduous challenge for process discovery algorithms(More)
—Recent years have witnessed the ability to gather an enormous amount of data in a large number of domains. Also in the field of business process management, there exists an urgent need to beneficially use these data to retrieve actionable knowledge about the actual way of working in the context of a certain business process. The research field concerned is(More)