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

A. Syamsiyah; B.F. van Dongen; W.M.P. van der Aalst

doi:10.1007/978-3-319-59466-8_4

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

A. Syamsiyah, B.F. van Dongen, W.M.P. van der Aalst

Process Science

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

6 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 language	English
Title of host publication	Enterprise, Business-Process and Information Systems Modeling
Subtitle of host publication	18th International Conference, BPMDS 2017, 22nd International Conference, EMMSAD 2017, Held at CAiSE 2017, Essen, Germany, June 12-13, 2017, Proceedings
Editors	I. Reinhartz-Berger, J. Gulden, S. Nurcan, W. Guédria, P. Bera
Place of Publication	Dordrecht
Publisher	Springer
Pages	51-67
Number of pages	17
ISBN (Electronic)	978-3-319-59466-8
ISBN (Print)	9783319594651
DOIs	https://doi.org/10.1007/978-3-319-59466-8_4
Publication status	Published - 17 May 2017
Event	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 Duration: 12 Jun 2017 → 13 Jun 2017

Publication series

Name	Lecture Notes in Business Information Processing
Volume	287
ISSN (Print)	1865-1348

Conference

Conference	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
Country/Territory	Germany
City	Essen
Period	12/06/17 → 13/06/17

Keywords

Process mining
Relational database
Repetitive discovery
Social network

Access to Document

10.1007/978-3-319-59466-8_4

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). 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).

@inproceedings{35f09369f5a34408884d38b61cbde223,

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

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. ",

keywords = "Process mining, Relational database, Repetitive discovery, Social network",

author = "A. Syamsiyah and {van Dongen}, B.F. and {van der Aalst}, W.M.P.",

year = "2017",

month = may,

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doi = "10.1007/978-3-319-59466-8_4",

language = "English",

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series = "Lecture Notes in Business Information Processing",

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pages = "51--67",

editor = "I. Reinhartz-Berger and J. Gulden and S. Nurcan and W. Gu{\'e}dria and P. Bera",

booktitle = "Enterprise, Business-Process and Information Systems Modeling",

address = "Germany",

note = "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 ; Conference date: 12-06-2017 Through 13-06-2017",

}

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 proceeding › Conference contribution › Academic › peer-review

TY - GEN

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T2 - 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

AU - Syamsiyah, A.

AU - van Dongen, B.F.

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

PY - 2017/5/17

Y1 - 2017/5/17

N2 - 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.

AB - 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.

KW - Process mining

KW - Relational database

KW - Repetitive discovery

KW - Social network

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M3 - Conference contribution

AN - SCOPUS:85021213910

SN - 9783319594651

T3 - Lecture Notes in Business Information Processing

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BT - Enterprise, Business-Process and Information Systems Modeling

A2 - Reinhartz-Berger, I.

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A2 - Bera, P.

PB - Springer

CY - Dordrecht

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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). doi: 10.1007/978-3-319-59466-8_4

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

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