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

Onderzoeksoutput: Hoofdstuk in Boek/Rapport/CongresprocedureConferentiebijdrageAcademicpeer review

34 Citaten (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.

Originele taal-2Engels
TitelInformation Systems in the Big Data Era - CAiSE Forum 2018, Proceedings
RedacteurenJ. Mendling, H. Mouratidis
Aantal pagina's18
ISBN van geprinte versie9783319929002
StatusGepubliceerd - 1 jan. 2018
Evenement30th International Conference on Advanced Information Systems Engineering (CAiSE 2018) - Tallinn, Estland
Duur: 11 jun. 201815 jun. 2018
Congresnummer: 30

Publicatie series

NaamLecture Notes in Business Information Processing
ISSN van geprinte versie1865-1348


Congres30th International Conference on Advanced Information Systems Engineering (CAiSE 2018)
Verkorte titelCAiSE 2018
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