Process mining aims to infer meaningful insights from process-related data and attracted the attention of practitioners, tool-vendors, and researchers in recent years. Traditionally, event logs are assumed to describe the as-is situation. But this is not necessarily the case in environments where logging may be compromised due to manual logging. For example, hospital staff may need to manually enter information regarding the patient’s treatment. As a result, events or timestamps may be missing or incorrect.
In this work, we make use of process knowledge captured in process models, and provide a method to repair missing events in the logs. This way, we facilitate analysis of incomplete logs. We realize the repair by combining stochastic Petri nets, alignments, and Bayesian networks.
Keywords: process mining; missing data; stochastic Petri nets; Bayesian networks
|Title of host publication||On the Move to Meaningful Internet Systems: OTM 2013 Workshops : Confederated International Workshops: OTM Academy, OTM Industry Case Studies Program, ACM, EI2N, ISDE, META4eS, ORM, SeDeS, SINCOM, SMS, and SOMOCO 2013, Graz, Austria, September 9 - 13, 2013, Proceedings|
|Editors||Y.T. Demey, H. Panetto|
|Place of Publication||Berlin|
|Publication status||Published - 2013|
|Name||Lecture Notes in Computer Science|