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2018 | OriginalPaper | Chapter

Extracting Object-Centric Event Logs to Support Process Mining on Databases

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

Published in: Information Systems in the Big Data Era

Publisher: Springer International Publishing

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Abstract

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.

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previous chapter Towards Reliable Predictive Process Monitoring

next chapter Q-Rapids Tool Prototype: Supporting Decision-Makers in Managing Quality in Rapid Software Development

Data Convergence means the same event is related to multiple process instances at once. Data divergence means that multiple events of the same activity are related to one process instance, i.e., the same activity is performed multiple times for the same process instance (cf. Fig. 4).

The behavioral perspective refers to the control-flow of events while the data perspective refers to data attributes.

XOC is pronounced as /sɒk/.

\(\mathcal P(X)\) is the power set of X, i.e., \(Y \in \mathcal P(X)\) if \(Y \subseteq X\).

\(tl^k(\sigma )\) means to get the last k elements of a sequence \(\sigma \).

The involved data and more real life examples can be found at http://www.win.tue.nl/ocbc/.

The notation for the negative relation in OCBC models is different from that in declare models.

http://www.processmining.org/xesame/start.

IEEE Standard for eXtensible Event Stream (XES) for Achieving Interoperability in Event Logs and Event Streams. IEEE Std. 1849-2016, pp. i–48 (2016)

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Title: Extracting Object-Centric Event Logs to Support Process Mining on Databases
Authors: Guangming Li
Eduardo González López de Murillas
Renata Medeiros de Carvalho
Wil M. P. van der Aalst
Publisher: Springer International Publishing
Book: Information Systems in the Big Data Era
Print ISBN: 978-3-319-92900-2

Electronic ISBN: 978-3-319-92901-9

Copyright Year: 2018
DOI: https://doi.org/10.1007/978-3-319-92901-9_16

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