Filtering spurious events from event streams of business processes

Sebastiaan J. van Zelst; Mohammadreza Fani Sani; Alireza Ostovar; Raffaele Conforti; Marcello La Rosa

doi:10.1007/978-3-319-91563-0_3

Filtering spurious events from event streams of business processes

Sebastiaan J. van Zelst, Mohammadreza Fani Sani, Alireza Ostovar, Raffaele Conforti, Marcello La Rosa

Process Science

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

23 Citations (Scopus)

Abstract

Process mining aims at gaining insights into business processes by analysing event data recorded during process execution. The majority of existing process mining techniques works offline, i.e. using static, historical data stored in event logs. Recently, the notion of online process mining has emerged, whereby techniques are applied on live event streams, as process executions unfold. Analysing event streams allows us to gain instant insights into business processes. However, current techniques assume the input stream to be completely free of noise and other anomalous behaviours. Hence, applying these techniques to real data leads to results of inferior quality. In this paper, we propose an event processor that enables us to filter out spurious events from a live event stream. Our experiments show that we are able to effectively filter out spurious events from the input stream and, as such, enhance online process mining results.

Original language	English
Title of host publication	Advanced Information Systems Engineering - 30th International Conference, CAiSE 2018, Proceedings
Publisher	Springer
Pages	35-52
Number of pages	18
ISBN (Print)	9783319915623
DOIs	https://doi.org/10.1007/978-3-319-91563-0_3
Publication status	Published - 1 Jan 2018
Event	30th International Conference on Advanced Information Systems Engineering, CAiSE 2018 - Tallinn, Estonia Duration: 11 Jun 2018 → 15 Jun 2018 Conference number: 30 https://caise2018.ut.ee/

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	10816 LNCS
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	30th International Conference on Advanced Information Systems Engineering, CAiSE 2018
Abbreviated title	CAiSE 2018
Country/Territory	Estonia
City	Tallinn
Period	11/06/18 → 15/06/18
Internet address	https://caise2018.ut.ee/

Keywords

Anomaly detection
Event stream
Filtering
Process mining

Access to Document

10.1007/978-3-319-91563-0_3

Cite this

van Zelst, S. J., Fani Sani, M., Ostovar, A., Conforti, R., & La Rosa, M. (2018). Filtering spurious events from event streams of business processes. In Advanced Information Systems Engineering - 30th International Conference, CAiSE 2018, Proceedings (pp. 35-52). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 10816 LNCS). Springer. https://doi.org/10.1007/978-3-319-91563-0_3

van Zelst, Sebastiaan J. ; Fani Sani, Mohammadreza ; Ostovar, Alireza et al. / Filtering spurious events from event streams of business processes. Advanced Information Systems Engineering - 30th International Conference, CAiSE 2018, Proceedings. Springer, 2018. pp. 35-52 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

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abstract = "Process mining aims at gaining insights into business processes by analysing event data recorded during process execution. The majority of existing process mining techniques works offline, i.e. using static, historical data stored in event logs. Recently, the notion of online process mining has emerged, whereby techniques are applied on live event streams, as process executions unfold. Analysing event streams allows us to gain instant insights into business processes. However, current techniques assume the input stream to be completely free of noise and other anomalous behaviours. Hence, applying these techniques to real data leads to results of inferior quality. In this paper, we propose an event processor that enables us to filter out spurious events from a live event stream. Our experiments show that we are able to effectively filter out spurious events from the input stream and, as such, enhance online process mining results.",

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van Zelst, SJ, Fani Sani, M, Ostovar, A, Conforti, R & La Rosa, M 2018, Filtering spurious events from event streams of business processes. in Advanced Information Systems Engineering - 30th International Conference, CAiSE 2018, Proceedings. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 10816 LNCS, Springer, pp. 35-52, 30th International Conference on Advanced Information Systems Engineering, CAiSE 2018, Tallinn, Estonia, 11/06/18. https://doi.org/10.1007/978-3-319-91563-0_3

Filtering spurious events from event streams of business processes. / van Zelst, Sebastiaan J.; Fani Sani, Mohammadreza; Ostovar, Alireza et al.
Advanced Information Systems Engineering - 30th International Conference, CAiSE 2018, Proceedings. Springer, 2018. p. 35-52 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 10816 LNCS).

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

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AU - van Zelst, Sebastiaan J.

AU - Fani Sani, Mohammadreza

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AU - Conforti, Raffaele

AU - La Rosa, Marcello

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AB - Process mining aims at gaining insights into business processes by analysing event data recorded during process execution. The majority of existing process mining techniques works offline, i.e. using static, historical data stored in event logs. Recently, the notion of online process mining has emerged, whereby techniques are applied on live event streams, as process executions unfold. Analysing event streams allows us to gain instant insights into business processes. However, current techniques assume the input stream to be completely free of noise and other anomalous behaviours. Hence, applying these techniques to real data leads to results of inferior quality. In this paper, we propose an event processor that enables us to filter out spurious events from a live event stream. Our experiments show that we are able to effectively filter out spurious events from the input stream and, as such, enhance online process mining results.

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van Zelst SJ, Fani Sani M, Ostovar A, Conforti R, La Rosa M. Filtering spurious events from event streams of business processes. In Advanced Information Systems Engineering - 30th International Conference, CAiSE 2018, Proceedings. Springer. 2018. p. 35-52. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/978-3-319-91563-0_3

Filtering spurious events from event streams of business processes

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Keywords

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