Extracting object-centric event logs to support process mining on databases

Guangming Li, Eduardo González López de Murillas, Renata Medeiros de Carvalho, Wil M.P. van der Aalst

Research output: Chapter in Book/Report/Conference proceedingConference contributionAcademicpeer-review

33 Citations (Scopus)
1 Downloads (Pure)


Process mining helps organizations to investigate how their operational processes are executed and how these can be improved. Process mining requires event logs extracted from information systems supporting these processes. The eXtensible Event Stream (XES) format is the current standard which requires a case notion to correlate events. However, it has problems to deal with object-centric data (e.g., database tables) due to the existence of one-to-many and many-to-many relations. In this paper, we propose an approach to extract, transform and store object-centric data, resulting in eXtensible Object-Centric (XOC) event logs. The XOC format does not require a case notion to avoid flattening multi-dimensional data. Besides, based on so-called object models which represent the states of a database, a XOC log can reveal the evolution of the database along with corresponding events. Dealing with object-centric data enables new process mining techniques that are able to capture the real processes much better.

Original languageEnglish
Title of host publicationInformation Systems in the Big Data Era - CAiSE Forum 2018, Proceedings
EditorsJ. Mendling, H. Mouratidis
Number of pages18
ISBN (Print)9783319929002
Publication statusPublished - 1 Jan 2018
Event30th International Conference on Advanced Information Systems Engineering (CAiSE 2018) - Tallinn, Estonia
Duration: 11 Jun 201815 Jun 2018
Conference number: 30

Publication series

NameLecture Notes in Business Information Processing
ISSN (Print)1865-1348


Conference30th International Conference on Advanced Information Systems Engineering (CAiSE 2018)
Abbreviated titleCAiSE 2018
Internet address


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