Recurrent process mining with live event data

Research output: Chapter in Book/Report/Conference proceedingConference contributionAcademicpeer-review

2 Citations (Scopus)
3 Downloads (Pure)

Abstract

In organizations, process mining activities are typically performed in a recurrent fashion, e.g. once a week, an event log is extracted from the information systems and a process mining tool is used to analyze the process’ characteristics. Typically, process mining tools import the data from a file-based source in a pre-processing step, followed by an actual process discovery step over the pre-processed data in order to present results to the analyst. As the amount of event data grows over time, these tools take more and more time to do pre-processing and all this time, the business analyst has to wait for the tool to finish. In this paper, we consider the problem of recurrent process discovery in live environments, i.e. in environments where event data can be extracted from information systems near real time. We present a method that pre-processes each event when it is being generated, so that the business analyst has the pre-processed data at his/her disposal when starting the analysis. To this end, we define a notion of intermediate structure between the underlying data and the layer where the actual mining is performed. This intermediate structure is kept in a persistent storage and is kept live under updates. Using a state of the art process mining technique, we show the feasibility of our approach. Our work is implemented in the process mining tool ProM using a relational database system as our persistent storage. Experiments are presented on real-life event data to compare the performance of the proposed approach with the state of the art.

Original languageEnglish
Title of host publicationBusiness Process Management Workshops
Subtitle of host publicationBPM 2017 International Workshops, Barcelona, Spain, September 10-11, 2017, Revised Papers
EditorsE. Teniente, M. Weidlich
Place of PublicationDordrecht
PublisherSpringer
Pages178-190
Number of pages13
ISBN (Electronic)978-3-319-74030-0
ISBN (Print)978-3-319-74029-4
DOIs
Publication statusPublished - 2018
Event15th International Conference on Business Process Management (BPM 2017) - Barcelona, Spain
Duration: 10 Sep 201715 Sep 2017
Conference number: 15
https://bpm2017.cs.upc.edu/

Publication series

NameLecture Notes in Business Information Processing
Volume308
ISSN (Print)1865-1348

Conference

Conference15th International Conference on Business Process Management (BPM 2017)
Abbreviated titleBPM 2017
CountrySpain
CityBarcelona
Period10/09/1715/09/17
Internet address

Fingerprint

Process Mining
Information systems
Preprocessing
Information Systems
Relational database systems
Processing
Database Systems
Relational Database
Process mining
Industry
Mining
Update
Analysts
Experiment
Experiments

Keywords

  • Incremental process discovery
  • Live event data
  • Recurrent process mining

Cite this

Syamsiyah, A., van Dongen, B. F., & van der Aalst, W. M. P. (2018). Recurrent process mining with live event data. In E. Teniente, & M. Weidlich (Eds.), Business Process Management Workshops: BPM 2017 International Workshops, Barcelona, Spain, September 10-11, 2017, Revised Papers (pp. 178-190). (Lecture Notes in Business Information Processing; Vol. 308). Dordrecht: Springer. https://doi.org/10.1007/978-3-319-74030-0_13
Syamsiyah, A. ; van Dongen, B.F. ; van der Aalst, W.M.P. / Recurrent process mining with live event data. Business Process Management Workshops: BPM 2017 International Workshops, Barcelona, Spain, September 10-11, 2017, Revised Papers. editor / E. Teniente ; M. Weidlich. Dordrecht : Springer, 2018. pp. 178-190 (Lecture Notes in Business Information Processing).
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Syamsiyah, A, van Dongen, BF & van der Aalst, WMP 2018, Recurrent process mining with live event data. in E Teniente & M Weidlich (eds), Business Process Management Workshops: BPM 2017 International Workshops, Barcelona, Spain, September 10-11, 2017, Revised Papers. Lecture Notes in Business Information Processing, vol. 308, Springer, Dordrecht, pp. 178-190, 15th International Conference on Business Process Management (BPM 2017), Barcelona, Spain, 10/09/17. https://doi.org/10.1007/978-3-319-74030-0_13

Recurrent process mining with live event data. / Syamsiyah, A.; van Dongen, B.F.; van der Aalst, W.M.P.

Business Process Management Workshops: BPM 2017 International Workshops, Barcelona, Spain, September 10-11, 2017, Revised Papers. ed. / E. Teniente; M. Weidlich. Dordrecht : Springer, 2018. p. 178-190 (Lecture Notes in Business Information Processing; Vol. 308).

Research output: Chapter in Book/Report/Conference proceedingConference contributionAcademicpeer-review

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AU - Syamsiyah, A.

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N2 - In organizations, process mining activities are typically performed in a recurrent fashion, e.g. once a week, an event log is extracted from the information systems and a process mining tool is used to analyze the process’ characteristics. Typically, process mining tools import the data from a file-based source in a pre-processing step, followed by an actual process discovery step over the pre-processed data in order to present results to the analyst. As the amount of event data grows over time, these tools take more and more time to do pre-processing and all this time, the business analyst has to wait for the tool to finish. In this paper, we consider the problem of recurrent process discovery in live environments, i.e. in environments where event data can be extracted from information systems near real time. We present a method that pre-processes each event when it is being generated, so that the business analyst has the pre-processed data at his/her disposal when starting the analysis. To this end, we define a notion of intermediate structure between the underlying data and the layer where the actual mining is performed. This intermediate structure is kept in a persistent storage and is kept live under updates. Using a state of the art process mining technique, we show the feasibility of our approach. Our work is implemented in the process mining tool ProM using a relational database system as our persistent storage. Experiments are presented on real-life event data to compare the performance of the proposed approach with the state of the art.

AB - In organizations, process mining activities are typically performed in a recurrent fashion, e.g. once a week, an event log is extracted from the information systems and a process mining tool is used to analyze the process’ characteristics. Typically, process mining tools import the data from a file-based source in a pre-processing step, followed by an actual process discovery step over the pre-processed data in order to present results to the analyst. As the amount of event data grows over time, these tools take more and more time to do pre-processing and all this time, the business analyst has to wait for the tool to finish. In this paper, we consider the problem of recurrent process discovery in live environments, i.e. in environments where event data can be extracted from information systems near real time. We present a method that pre-processes each event when it is being generated, so that the business analyst has the pre-processed data at his/her disposal when starting the analysis. To this end, we define a notion of intermediate structure between the underlying data and the layer where the actual mining is performed. This intermediate structure is kept in a persistent storage and is kept live under updates. Using a state of the art process mining technique, we show the feasibility of our approach. Our work is implemented in the process mining tool ProM using a relational database system as our persistent storage. Experiments are presented on real-life event data to compare the performance of the proposed approach with the state of the art.

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M3 - Conference contribution

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SN - 978-3-319-74029-4

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SP - 178

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BT - Business Process Management Workshops

A2 - Teniente, E.

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PB - Springer

CY - Dordrecht

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Syamsiyah A, van Dongen BF, van der Aalst WMP. Recurrent process mining with live event data. In Teniente E, Weidlich M, editors, Business Process Management Workshops: BPM 2017 International Workshops, Barcelona, Spain, September 10-11, 2017, Revised Papers. Dordrecht: Springer. 2018. p. 178-190. (Lecture Notes in Business Information Processing). https://doi.org/10.1007/978-3-319-74030-0_13