Toward an Automated Labeling of Event Log Attributes

Amine Abbad Andaloussi, Andrea Burattin, Barbara Weber

Research output: Chapter in Book/Report/Conference proceedingArticle in proceedingsResearchpeer-review

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Process mining aims at exploring the data produced by executable business processes to mine the underlying control-flow and dataflow. Most of the process mining algorithms assume the existence of an event log with a certain maturity level. Unfortunately, the logs provided by process unaware information systems often do not comply with the required maturity level, since they lack the notion of process instance, also referred in process mining as “case id”. Without a proper identification of the case id attribute in log files, the outcome of process mining algorithms is unpredictable. This paper proposes a new approach that aims to overcome this challenge by automatically inferring the case id attribute from log files. The approach has been implemented as a ProM plugin and evaluated with several real-world event logs. The results demonstrate a high accuracy in inferring the case id attribute.
Original languageEnglish
Title of host publicationBPMDS 2018 : Business Process Modeling, Development, and Support
Publication date2018
ISBN (Print)9783319917030
Publication statusPublished - 2018
EventBPMDS’18 Working Conference - Tallinn, Estonia
Duration: 11 Jun 201812 Jun 2018


ConferenceBPMDS’18 Working Conference
Internet address
SeriesLecture Notes in Business Information Processing


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