Predicting deadline tansgressions using event logs

Pika, Anastasiia, van der Aalst, Wil M.P., Fidge, Colin J., ter Hofstede, Arthur H.M., & Wynn, Moe T. (2012) Predicting deadline tansgressions using event logs. In Lecture Notes in Business Information Processing, Springer, Tallin, Estonia, pp. 211-216.

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Effective risk management is crucial for any organisation. One of its key steps is risk identification, but few tools exist to support this process. Here we present a method for the automatic discovery of a particular type of process-related risk, the danger of deadline transgressions or overruns, based on the analysis of event logs. We define a set of time-related process risk indicators, i.e., patterns observable in event logs that highlight the likelihood of an overrun, and then show how instances of these patterns can be identified automatically using statistical principles. To demonstrate its feasibility, the approach has been implemented as a plug-in module to the process mining framework ProM and tested using an event log from a Dutch financial institution.

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13 citations in Scopus
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ID Code: 54057
Item Type: Conference Paper
Refereed: Yes
Keywords: process mining, risk identification, business process management
DOI: 10.1007/978-3-642-36285-9_22
ISSN: 1865-1348
Subjects: Australian and New Zealand Standard Research Classification > INFORMATION AND COMPUTING SCIENCES (080000) > INFORMATION SYSTEMS (080600)
Divisions: Current > Schools > School of Information Systems
Current > QUT Faculties and Divisions > Science & Engineering Faculty
Deposited On: 10 Oct 2012 02:49
Last Modified: 12 Apr 2013 05:15

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