Predicting deadline transgressions using event logs

A. Pika; W.M.P. Aalst, van der; C.J. Fidge; A.H.M. Hofstede, ter; M.T. Wynn

doi:10.1007/978-3-642-36285-9_22

Predicting deadline transgressions using event logs

A. Pika, W.M.P. Aalst, van der, C.J. Fidge, A.H.M. Hofstede, ter, M.T. Wynn

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › Academic › peer-review

45 Citations (Scopus)

1 Downloads (Pure)

Abstract

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.

Original language	English
Title of host publication	Business Process Management Workshops : BPM 2012 International Workshops, Tallinn, Estonia, September 3, 2012. Revised Papers
Editors	M. La Rosa, P. Soffer
Place of Publication	Berlin
Publisher	Springer
Pages	211-216
ISBN (Print)	978-3-642-36284-2
DOIs	https://doi.org/10.1007/978-3-642-36285-9_22
Publication status	Published - 2013
Event	8th International Workshop on Business Process Intelligence (BPI 2012) - Tallinn, Estonia Duration: 3 Sept 2012 → 3 Sept 2012 Conference number: 8

Publication series

Name	Lecture Notes in Business Information Processing
Volume	132
ISSN (Print)	1865-1348

Workshop

Workshop	8th International Workshop on Business Process Intelligence (BPI 2012)
Abbreviated title	BPI 2012
Country/Territory	Estonia
City	Tallinn
Period	3/09/12 → 3/09/12
Other	Workshop held in conjunction with the 10th International Conference on Business Process Management (BPM 2012)

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10.1007/978-3-642-36285-9_22

Cite this

Pika, A., Aalst, van der, W. M. P., Fidge, C. J., Hofstede, ter, A. H. M., & Wynn, M. T. (2013). Predicting deadline transgressions using event logs. In M. La Rosa, & P. Soffer (Eds.), Business Process Management Workshops : BPM 2012 International Workshops, Tallinn, Estonia, September 3, 2012. Revised Papers (pp. 211-216). (Lecture Notes in Business Information Processing; Vol. 132). Springer. https://doi.org/10.1007/978-3-642-36285-9_22

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title = "Predicting deadline transgressions using event logs",

abstract = "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.",

author = "A. Pika and {Aalst, van der}, W.M.P. and C.J. Fidge and {Hofstede, ter}, A.H.M. and M.T. Wynn",

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booktitle = "Business Process Management Workshops : BPM 2012 International Workshops, Tallinn, Estonia, September 3, 2012. Revised Papers",

address = "Germany",

note = "8th International Workshop on Business Process Intelligence (BPI 2012), BPI 2012 ; Conference date: 03-09-2012 Through 03-09-2012",

}

Pika, A, Aalst, van der, WMP, Fidge, CJ, Hofstede, ter, AHM & Wynn, MT 2013, Predicting deadline transgressions using event logs. in M La Rosa & P Soffer (eds), Business Process Management Workshops : BPM 2012 International Workshops, Tallinn, Estonia, September 3, 2012. Revised Papers. Lecture Notes in Business Information Processing, vol. 132, Springer, Berlin, pp. 211-216, 8th International Workshop on Business Process Intelligence (BPI 2012), Tallinn, Estonia, 3/09/12. https://doi.org/10.1007/978-3-642-36285-9_22

Predicting deadline transgressions using event logs. / Pika, A.; Aalst, van der, W.M.P.; Fidge, C.J. et al.
Business Process Management Workshops : BPM 2012 International Workshops, Tallinn, Estonia, September 3, 2012. Revised Papers. ed. / M. La Rosa; P. Soffer. Berlin: Springer, 2013. p. 211-216 (Lecture Notes in Business Information Processing; Vol. 132).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › Academic › peer-review

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AU - Pika, A.

AU - Aalst, van der, W.M.P.

AU - Fidge, C.J.

AU - Hofstede, ter, A.H.M.

AU - Wynn, M.T.

N1 - Conference code: 8

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N2 - 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.

AB - 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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SN - 978-3-642-36284-2

T3 - Lecture Notes in Business Information Processing

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BT - Business Process Management Workshops : BPM 2012 International Workshops, Tallinn, Estonia, September 3, 2012. Revised Papers

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PB - Springer

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T2 - 8th International Workshop on Business Process Intelligence (BPI 2012)

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Pika A, Aalst, van der WMP, Fidge CJ, Hofstede, ter AHM, Wynn MT. Predicting deadline transgressions using event logs. In La Rosa M, Soffer P, editors, Business Process Management Workshops : BPM 2012 International Workshops, Tallinn, Estonia, September 3, 2012. Revised Papers. Berlin: Springer. 2013. p. 211-216. (Lecture Notes in Business Information Processing). doi: 10.1007/978-3-642-36285-9_22

Predicting deadline transgressions using event logs

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