Discovering social networks instantly: Moving process mining computations to the database and data entry time

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

3 Citations (Scopus)
2 Downloads (Pure)

Abstract

Process mining aims to turn event data into insights and actions in order to improve processes. To improve process performance it is crucial to get insights into the way people work and collaborate. In this paper, we focus on discovering social networks from event data.
To be able to deal with large data sets or with an environment which requires repetitive discoveries during the analysis, and still provide results instantly, we use an approach where most of the computation is moved to the database and things are precomputed at data entry time.
Differently from traditional process mining where event data is stored in file-based system, we store event data in relational databases. Moreover, the database also has a role as an engine to compute the intermediate structure of social network during insertion data.
By moving computation both in location (to database) and time (to recording time), the discovery of social networks in a process context becomes truly scalable. The approach has been implemented using the open source process mining toolkit ProM. The experiments reported in this paper demonstrate scalability while providing results instantly.
Original languageEnglish
Title of host publicationEnterprise, Business-Process and Information Systems Modeling
Subtitle of host publication18th International Conference, BPMDS 2017, 22nd International Conference, EMMSAD 2017, Held at CAiSE 2017, Essen, Germany, June 12-13, 2017, Proceedings
EditorsI. Reinhartz-Berger, J. Gulden, S. Nurcan, W. Guédria, P. Bera
Place of PublicationDordrecht
PublisherSpringer
Pages51-67
Number of pages17
ISBN (Electronic)978-3-319-59466-8
ISBN (Print)9783319594651
DOIs
Publication statusPublished - 17 May 2017
Event18th International Conference on Business Process Modeling, Development and Support, BPMDS 2017 and 22nd International Conference on Evaluation and Modeling Methods for Systems Analysis and Development, EMMSAD 2017 held at Conference on Advanced Information Systems Engineering, CAiSE 2017 - Essen, Germany
Duration: 12 Jun 201713 Jun 2017

Publication series

NameLecture Notes in Business Information Processing
Volume287
ISSN (Print)1865-1348

Conference

Conference18th International Conference on Business Process Modeling, Development and Support, BPMDS 2017 and 22nd International Conference on Evaluation and Modeling Methods for Systems Analysis and Development, EMMSAD 2017 held at Conference on Advanced Information Systems Engineering, CAiSE 2017
CountryGermany
CityEssen
Period12/06/1713/06/17

Fingerprint

Process Mining
Social Networks
Data acquisition
Scalability
Relational Database
Engines
Large Data Sets
Open Source
Thing
Insertion
Social networks
Process mining
Data base
Engine
Experiments
Demonstrate
Experiment

Keywords

  • Process mining
  • Relational database
  • Repetitive discovery
  • Social network

Cite this

Syamsiyah, A., van Dongen, B. F., & van der Aalst, W. M. P. (2017). Discovering social networks instantly: Moving process mining computations to the database and data entry time. In I. Reinhartz-Berger, J. Gulden, S. Nurcan, W. Guédria, & P. Bera (Eds.), Enterprise, Business-Process and Information Systems Modeling: 18th International Conference, BPMDS 2017, 22nd International Conference, EMMSAD 2017, Held at CAiSE 2017, Essen, Germany, June 12-13, 2017, Proceedings (pp. 51-67). (Lecture Notes in Business Information Processing; Vol. 287). Dordrecht: Springer. https://doi.org/10.1007/978-3-319-59466-8_4
Syamsiyah, A. ; van Dongen, B.F. ; van der Aalst, W.M.P. / Discovering social networks instantly : Moving process mining computations to the database and data entry time. Enterprise, Business-Process and Information Systems Modeling: 18th International Conference, BPMDS 2017, 22nd International Conference, EMMSAD 2017, Held at CAiSE 2017, Essen, Germany, June 12-13, 2017, Proceedings. editor / I. Reinhartz-Berger ; J. Gulden ; S. Nurcan ; W. Guédria ; P. Bera. Dordrecht : Springer, 2017. pp. 51-67 (Lecture Notes in Business Information Processing).
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abstract = "Process mining aims to turn event data into insights and actions in order to improve processes. To improve process performance it is crucial to get insights into the way people work and collaborate. In this paper, we focus on discovering social networks from event data. To be able to deal with large data sets or with an environment which requires repetitive discoveries during the analysis, and still provide results instantly, we use an approach where most of the computation is moved to the database and things are precomputed at data entry time. Differently from traditional process mining where event data is stored in file-based system, we store event data in relational databases. Moreover, the database also has a role as an engine to compute the intermediate structure of social network during insertion data. By moving computation both in location (to database) and time (to recording time), the discovery of social networks in a process context becomes truly scalable. The approach has been implemented using the open source process mining toolkit ProM. The experiments reported in this paper demonstrate scalability while providing results instantly.",
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Syamsiyah, A, van Dongen, BF & van der Aalst, WMP 2017, Discovering social networks instantly: Moving process mining computations to the database and data entry time. in I Reinhartz-Berger, J Gulden, S Nurcan, W Guédria & P Bera (eds), Enterprise, Business-Process and Information Systems Modeling: 18th International Conference, BPMDS 2017, 22nd International Conference, EMMSAD 2017, Held at CAiSE 2017, Essen, Germany, June 12-13, 2017, Proceedings. Lecture Notes in Business Information Processing, vol. 287, Springer, Dordrecht, pp. 51-67, 18th International Conference on Business Process Modeling, Development and Support, BPMDS 2017 and 22nd International Conference on Evaluation and Modeling Methods for Systems Analysis and Development, EMMSAD 2017 held at Conference on Advanced Information Systems Engineering, CAiSE 2017, Essen, Germany, 12/06/17. https://doi.org/10.1007/978-3-319-59466-8_4

Discovering social networks instantly : Moving process mining computations to the database and data entry time. / Syamsiyah, A.; van Dongen, B.F.; van der Aalst, W.M.P.

Enterprise, Business-Process and Information Systems Modeling: 18th International Conference, BPMDS 2017, 22nd International Conference, EMMSAD 2017, Held at CAiSE 2017, Essen, Germany, June 12-13, 2017, Proceedings. ed. / I. Reinhartz-Berger; J. Gulden; S. Nurcan; W. Guédria; P. Bera. Dordrecht : Springer, 2017. p. 51-67 (Lecture Notes in Business Information Processing; Vol. 287).

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

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Syamsiyah A, van Dongen BF, van der Aalst WMP. Discovering social networks instantly: Moving process mining computations to the database and data entry time. In Reinhartz-Berger I, Gulden J, Nurcan S, Guédria W, Bera P, editors, Enterprise, Business-Process and Information Systems Modeling: 18th International Conference, BPMDS 2017, 22nd International Conference, EMMSAD 2017, Held at CAiSE 2017, Essen, Germany, June 12-13, 2017, Proceedings. Dordrecht: Springer. 2017. p. 51-67. (Lecture Notes in Business Information Processing). https://doi.org/10.1007/978-3-319-59466-8_4