Supporting Event Log Extraction based on Matching

Vinicius Stein Dani; Henrik Leopold; Jan Martijn van der Werf; Hajo Reijers

doi:10.1007/978-3-031-25383-6_24

Supporting Event Log Extraction based on Matching

Vinicius Stein Dani^*, Henrik Leopold, Jan Martijn van der Werf, Hajo Reijers

^*Corresponding author for this work

Kühne Logistics University

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

Abstract

Process mining allows organizations to obtain relevant insights into the execution of their processes. However, the starting point of any process mining analysis is an event log, which is typically not readily available in practice. The extraction of event logs from the relevant databases is a manual and highly time-consuming task, and often a hurdle for the application of process mining altogether. Available support for event log extraction comes with different assumptions and requirements and only provides limited automated support. In this paper, we therefore take a novel angle at supporting event log extraction. The core idea of our paper is to use an existing process model as a starting point and automatically identify to which database tables the activities of the considered process model relate to. Based on the resulting mapping, an event log can then be extracted in an automated fashion. We use this paper to define a first approach that is able to identify such a mapping between a process model and a database. We evaluate our approach using three real-world databases and five process models from the purchase-to-pay domain. The results of our evaluation show that our approach has the potential to successfully support event log extraction based on matching.

Original language	English
Title of host publication	Business Process Management Workshops
Subtitle of host publication	BPM 2022 International Workshops, Münster, Germany, September 11–16, 2022, Revised Selected Papers
Editors	Cristina Cabanillas, Niels Frederik Garmann-Johnsen, Agnes Koschmider
Publisher	Springer
Pages	322-333
Number of pages	12
Edition	1
ISBN (Electronic)	978-3-031-25383-6
ISBN (Print)	978-3-031-25382-9
DOIs	https://doi.org/10.1007/978-3-031-25383-6_24
Publication status	Published - 11 Feb 2023
Event	1st Workshop on Natural Language Processing for Business Process Management - Münster, Germany Duration: 12 Sept 2022 → … https://bpm2022.uni-muenster.de/workshops/1st-workshop-natural-language-processing-for-business-process-management-nlp4bpm

Publication series

Name	Lecture Notes in Business Information Processing
Publisher	Springer
Volume	460
ISSN (Print)	1865-1348
ISSN (Electronic)	1865-1356

Workshop

Workshop	1st Workshop on Natural Language Processing for Business Process Management
Abbreviated title	NLP4BPM
Country/Territory	Germany
City	Münster
Period	12/09/22 → …
Internet address	https://bpm2022.uni-muenster.de/workshops/1st-workshop-natural-language-processing-for-business-process-management-nlp4bpm

Bibliographical note

Funding Information:
Acknowledgements. Part of this research was funded by NWO (Netherlands Organisation for Scientific Research) project number 16672.

Publisher Copyright:
© 2023, Springer Nature Switzerland AG.

Keywords

Event log extraction
Natural language processing
Automated matching

Access to Document

10.1007/978-3-031-25383-6_24

978-3-031-25383-6_24Final published version, 470 KBLicence: Taverne

Cite this

Stein Dani, V., Leopold, H., van der Werf, J. M., & Reijers, H. (2023). Supporting Event Log Extraction based on Matching. In C. Cabanillas, N. F. Garmann-Johnsen, & A. Koschmider (Eds.), Business Process Management Workshops: BPM 2022 International Workshops, Münster, Germany, September 11–16, 2022, Revised Selected Papers (1 ed., pp. 322-333). (Lecture Notes in Business Information Processing; Vol. 460 ). Springer. https://doi.org/10.1007/978-3-031-25383-6_24

Stein Dani, Vinicius ; Leopold, Henrik ; van der Werf, Jan Martijn et al. / Supporting Event Log Extraction based on Matching. Business Process Management Workshops: BPM 2022 International Workshops, Münster, Germany, September 11–16, 2022, Revised Selected Papers. editor / Cristina Cabanillas ; Niels Frederik Garmann-Johnsen ; Agnes Koschmider. 1. ed. Springer, 2023. pp. 322-333 (Lecture Notes in Business Information Processing).

@inproceedings{f7d882b3579c4eebbb515709b40b16c8,

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abstract = "Process mining allows organizations to obtain relevant insights into the execution of their processes. However, the starting point of any process mining analysis is an event log, which is typically not readily available in practice. The extraction of event logs from the relevant databases is a manual and highly time-consuming task, and often a hurdle for the application of process mining altogether. Available support for event log extraction comes with different assumptions and requirements and only provides limited automated support. In this paper, we therefore take a novel angle at supporting event log extraction. The core idea of our paper is to use an existing process model as a starting point and automatically identify to which database tables the activities of the considered process model relate to. Based on the resulting mapping, an event log can then be extracted in an automated fashion. We use this paper to define a first approach that is able to identify such a mapping between a process model and a database. We evaluate our approach using three real-world databases and five process models from the purchase-to-pay domain. The results of our evaluation show that our approach has the potential to successfully support event log extraction based on matching.",

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note = "Funding Information: Acknowledgements. Part of this research was funded by NWO (Netherlands Organisation for Scientific Research) project number 16672. Publisher Copyright: {\textcopyright} 2023, Springer Nature Switzerland AG.; 1st Workshop on Natural Language Processing for Business Process Management, NLP4BPM ; Conference date: 12-09-2022",

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Stein Dani, V, Leopold, H, van der Werf, JM & Reijers, H 2023, Supporting Event Log Extraction based on Matching. in C Cabanillas, NF Garmann-Johnsen & A Koschmider (eds), Business Process Management Workshops: BPM 2022 International Workshops, Münster, Germany, September 11–16, 2022, Revised Selected Papers. 1 edn, Lecture Notes in Business Information Processing, vol. 460 , Springer, pp. 322-333, 1st Workshop on Natural Language Processing for Business Process Management, Münster, Germany, 12/09/22. https://doi.org/10.1007/978-3-031-25383-6_24

Supporting Event Log Extraction based on Matching. / Stein Dani, Vinicius; Leopold, Henrik; van der Werf, Jan Martijn et al.
Business Process Management Workshops: BPM 2022 International Workshops, Münster, Germany, September 11–16, 2022, Revised Selected Papers. ed. / Cristina Cabanillas; Niels Frederik Garmann-Johnsen; Agnes Koschmider. 1. ed. Springer, 2023. p. 322-333 (Lecture Notes in Business Information Processing; Vol. 460 ).

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

TY - GEN

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AU - Leopold, Henrik

AU - van der Werf, Jan Martijn

AU - Reijers, Hajo

N1 - Funding Information: Acknowledgements. Part of this research was funded by NWO (Netherlands Organisation for Scientific Research) project number 16672. Publisher Copyright: © 2023, Springer Nature Switzerland AG.

PY - 2023/2/11

Y1 - 2023/2/11

N2 - Process mining allows organizations to obtain relevant insights into the execution of their processes. However, the starting point of any process mining analysis is an event log, which is typically not readily available in practice. The extraction of event logs from the relevant databases is a manual and highly time-consuming task, and often a hurdle for the application of process mining altogether. Available support for event log extraction comes with different assumptions and requirements and only provides limited automated support. In this paper, we therefore take a novel angle at supporting event log extraction. The core idea of our paper is to use an existing process model as a starting point and automatically identify to which database tables the activities of the considered process model relate to. Based on the resulting mapping, an event log can then be extracted in an automated fashion. We use this paper to define a first approach that is able to identify such a mapping between a process model and a database. We evaluate our approach using three real-world databases and five process models from the purchase-to-pay domain. The results of our evaluation show that our approach has the potential to successfully support event log extraction based on matching.

AB - Process mining allows organizations to obtain relevant insights into the execution of their processes. However, the starting point of any process mining analysis is an event log, which is typically not readily available in practice. The extraction of event logs from the relevant databases is a manual and highly time-consuming task, and often a hurdle for the application of process mining altogether. Available support for event log extraction comes with different assumptions and requirements and only provides limited automated support. In this paper, we therefore take a novel angle at supporting event log extraction. The core idea of our paper is to use an existing process model as a starting point and automatically identify to which database tables the activities of the considered process model relate to. Based on the resulting mapping, an event log can then be extracted in an automated fashion. We use this paper to define a first approach that is able to identify such a mapping between a process model and a database. We evaluate our approach using three real-world databases and five process models from the purchase-to-pay domain. The results of our evaluation show that our approach has the potential to successfully support event log extraction based on matching.

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Stein Dani V, Leopold H, van der Werf JM , Reijers H. Supporting Event Log Extraction based on Matching. In Cabanillas C, Garmann-Johnsen NF, Koschmider A, editors, Business Process Management Workshops: BPM 2022 International Workshops, Münster, Germany, September 11–16, 2022, Revised Selected Papers. 1 ed. Springer. 2023. p. 322-333. (Lecture Notes in Business Information Processing). doi: 10.1007/978-3-031-25383-6_24

Supporting Event Log Extraction based on Matching

Abstract

Publication series

Workshop

Bibliographical note

Keywords

Access to Document

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