A native operator for process discovery

Alifah Syamsiyah; Boudewijn F. van Dongen; Remco M. Dijkman

doi:10.1007/978-3-319-98812-2_25

A native operator for process discovery

Alifah Syamsiyah, Boudewijn F. van Dongen, Remco M. Dijkman

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

3 Citations (Scopus)

2 Downloads (Pure)

Abstract

The goal of process mining is to gain insights into operational processes through the analysis of events recorded by information systems. Typically, this is done in three phases. Firstly, events are extracted from a data store into an event log. Secondly, an intermediate structure is built in memory and finally, this intermediate structure is converted into a process model or other analysis results. In this paper, we propose a native SQL operator for direct process discovery on relational databases. We merge steps 1 and 2 by defining a native operator for the simplest form of the intermediate structure, called the “directly follows relation”. We evaluate our work on big event data and the experimental results show that it performs faster than the state-of-the-art of database approaches.

Original language	English
Title of host publication	Database and Expert Systems Applications - 29th International Conference, DEXA 2018, Proceedings
Editors	Günther Pernul, Sven Hartmann, Hui Ma, Abdelkader Hameurlain, Roland R. Wagner
Publisher	Springer
Pages	292-300
Number of pages	9
ISBN (Print)	9783319988115
DOIs	https://doi.org/10.1007/978-3-319-98812-2_25
Publication status	Published - 1 Jan 2018
Event	29th International Conference on Database and Expert Systems Applications, (DEXA2018) - Regensburg, Germany Duration: 3 Sept 2018 → 6 Sept 2018 http://www.dexa.org/

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	11030 LNCS
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	29th International Conference on Database and Expert Systems Applications, (DEXA2018)
Abbreviated title	DEXA2018
Country/Territory	Germany
City	Regensburg
Period	3/09/18 → 6/09/18
Internet address	http://www.dexa.org/

Keywords

Process discovery
Relational database
SQL operator

Access to Document

10.1007/978-3-319-98812-2_25

Cite this

Syamsiyah, A., van Dongen, B. F., & Dijkman, R. M. (2018). A native operator for process discovery. In G. Pernul, S. Hartmann, H. Ma, A. Hameurlain, & R. R. Wagner (Eds.), Database and Expert Systems Applications - 29th International Conference, DEXA 2018, Proceedings (pp. 292-300). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 11030 LNCS). Springer. https://doi.org/10.1007/978-3-319-98812-2_25

Syamsiyah, Alifah ; van Dongen, Boudewijn F. ; Dijkman, Remco M. / A native operator for process discovery. Database and Expert Systems Applications - 29th International Conference, DEXA 2018, Proceedings. editor / Günther Pernul ; Sven Hartmann ; Hui Ma ; Abdelkader Hameurlain ; Roland R. Wagner. Springer, 2018. pp. 292-300 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

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abstract = "The goal of process mining is to gain insights into operational processes through the analysis of events recorded by information systems. Typically, this is done in three phases. Firstly, events are extracted from a data store into an event log. Secondly, an intermediate structure is built in memory and finally, this intermediate structure is converted into a process model or other analysis results. In this paper, we propose a native SQL operator for direct process discovery on relational databases. We merge steps 1 and 2 by defining a native operator for the simplest form of the intermediate structure, called the “directly follows relation”. We evaluate our work on big event data and the experimental results show that it performs faster than the state-of-the-art of database approaches.",

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Syamsiyah, A, van Dongen, BF & Dijkman, RM 2018, A native operator for process discovery. in G Pernul, S Hartmann, H Ma, A Hameurlain & RR Wagner (eds), Database and Expert Systems Applications - 29th International Conference, DEXA 2018, Proceedings. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 11030 LNCS, Springer, pp. 292-300, 29th International Conference on Database and Expert Systems Applications, (DEXA2018), Regensburg, Germany, 3/09/18. https://doi.org/10.1007/978-3-319-98812-2_25

A native operator for process discovery. / Syamsiyah, Alifah; van Dongen, Boudewijn F.; Dijkman, Remco M.
Database and Expert Systems Applications - 29th International Conference, DEXA 2018, Proceedings. ed. / Günther Pernul; Sven Hartmann; Hui Ma; Abdelkader Hameurlain; Roland R. Wagner. Springer, 2018. p. 292-300 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 11030 LNCS).

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

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T1 - A native operator for process discovery

AU - Syamsiyah, Alifah

AU - van Dongen, Boudewijn F.

AU - Dijkman, Remco M.

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N2 - The goal of process mining is to gain insights into operational processes through the analysis of events recorded by information systems. Typically, this is done in three phases. Firstly, events are extracted from a data store into an event log. Secondly, an intermediate structure is built in memory and finally, this intermediate structure is converted into a process model or other analysis results. In this paper, we propose a native SQL operator for direct process discovery on relational databases. We merge steps 1 and 2 by defining a native operator for the simplest form of the intermediate structure, called the “directly follows relation”. We evaluate our work on big event data and the experimental results show that it performs faster than the state-of-the-art of database approaches.

AB - The goal of process mining is to gain insights into operational processes through the analysis of events recorded by information systems. Typically, this is done in three phases. Firstly, events are extracted from a data store into an event log. Secondly, an intermediate structure is built in memory and finally, this intermediate structure is converted into a process model or other analysis results. In this paper, we propose a native SQL operator for direct process discovery on relational databases. We merge steps 1 and 2 by defining a native operator for the simplest form of the intermediate structure, called the “directly follows relation”. We evaluate our work on big event data and the experimental results show that it performs faster than the state-of-the-art of database approaches.

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BT - Database and Expert Systems Applications - 29th International Conference, DEXA 2018, Proceedings

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A2 - Hartmann, Sven

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Syamsiyah A, van Dongen BF , Dijkman RM. A native operator for process discovery. In Pernul G, Hartmann S, Ma H, Hameurlain A, Wagner RR, editors, Database and Expert Systems Applications - 29th International Conference, DEXA 2018, Proceedings. Springer. 2018. p. 292-300. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/978-3-319-98812-2_25

A native operator for process discovery

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Keywords

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