Andrea Burattin, Ph.D.

Assistant Professor

Toward an Automated Labeling of Event Log Attributes

Abstract

Journal cover

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.

Paper Information and Files

Published in Proceedings of BPMDS 2018; Tallin, Estonia; July 2018.

DOI: 10.1007/978-3-319-91704-7_6