Turning Logs Into Lumber: Preprocessing Tasks in Process Mining

Ying Liu; Vinicius Stein Dani; Iris Beerepoot; Xixi Lu

doi:10.1007/978-3-031-56107-8_8

Turning Logs Into Lumber: Preprocessing Tasks in Process Mining

Ying Liu
, Vinicius Stein Dani
, Iris Beerepoot
, Xixi Lu^*

^*Corresponding author for this work

Sub Process Science

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

Abstract

Event logs are invaluable for conducting process mining projects, offering insights into process improvement and data-driven decision-making. However, data quality issues affect the correctness and trustworthiness of these insights, making preprocessing tasks a necessity. Despite the recognized importance, the execution of preprocessing tasks remains ad-hoc, lacking support. This paper presents a systematic literature review that establishes a comprehensive repository of preprocessing tasks and their usage in case studies. We identify six high-level and 20 low-level preprocessing tasks in case studies. Log filtering, transformation, and abstraction are commonly used, while log enriching, integration, and reduction are less frequent. These results can be considered a first step in contributing to more structured, transparent event log preprocessing, enhancing process mining reliability.

Original language	English
Title of host publication	Process Mining Workshops - ICPM 2023 International Workshops, 2023, Revised Selected Papers
Subtitle of host publication	The Event Data & Behavioral Analytics (EdbA 2023)
Editors	Johannes De Smedt, Pnina Soffer
Publisher	Springer
Pages	98-109
Number of pages	12
ISBN (Print)	9783031561061
DOIs	https://doi.org/10.1007/978-3-031-56107-8_8
Publication status	Published - 13 Apr 2024

Publication series

Name	Lecture Notes in Business Information Processing
Volume	503 LNBIP
ISSN (Print)	1865-1348
ISSN (Electronic)	1865-1356

Bibliographical note

Publisher Copyright:
© The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

Keywords

Event log
Log preprocessing
Process mining

Access to Document

10.1007/978-3-031-56107-8_8

Cite this

Liu, Y., Stein Dani, V., Beerepoot, I., & Lu, X. (2024). Turning Logs Into Lumber: Preprocessing Tasks in Process Mining. In J. De Smedt, & P. Soffer (Eds.), Process Mining Workshops - ICPM 2023 International Workshops, 2023, Revised Selected Papers: The Event Data & Behavioral Analytics (EdbA 2023) (pp. 98-109). (Lecture Notes in Business Information Processing; Vol. 503 LNBIP). Springer. https://doi.org/10.1007/978-3-031-56107-8_8

Liu, Ying ; Stein Dani, Vinicius ; Beerepoot, Iris et al. / Turning Logs Into Lumber : Preprocessing Tasks in Process Mining. Process Mining Workshops - ICPM 2023 International Workshops, 2023, Revised Selected Papers: The Event Data & Behavioral Analytics (EdbA 2023). editor / Johannes De Smedt ; Pnina Soffer. Springer, 2024. pp. 98-109 (Lecture Notes in Business Information Processing).

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abstract = "Event logs are invaluable for conducting process mining projects, offering insights into process improvement and data-driven decision-making. However, data quality issues affect the correctness and trustworthiness of these insights, making preprocessing tasks a necessity. Despite the recognized importance, the execution of preprocessing tasks remains ad-hoc, lacking support. This paper presents a systematic literature review that establishes a comprehensive repository of preprocessing tasks and their usage in case studies. We identify six high-level and 20 low-level preprocessing tasks in case studies. Log filtering, transformation, and abstraction are commonly used, while log enriching, integration, and reduction are less frequent. These results can be considered a first step in contributing to more structured, transparent event log preprocessing, enhancing process mining reliability.",

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note = "Publisher Copyright: {\textcopyright} The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.",

year = "2024",

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Liu, Y, Stein Dani, V , Beerepoot, I & Lu, X 2024, Turning Logs Into Lumber: Preprocessing Tasks in Process Mining. in J De Smedt & P Soffer (eds), Process Mining Workshops - ICPM 2023 International Workshops, 2023, Revised Selected Papers: The Event Data & Behavioral Analytics (EdbA 2023). Lecture Notes in Business Information Processing, vol. 503 LNBIP, Springer, pp. 98-109. https://doi.org/10.1007/978-3-031-56107-8_8

Turning Logs Into Lumber: Preprocessing Tasks in Process Mining. / Liu, Ying; Stein Dani, Vinicius ; Beerepoot, Iris et al.
Process Mining Workshops - ICPM 2023 International Workshops, 2023, Revised Selected Papers: The Event Data & Behavioral Analytics (EdbA 2023). ed. / Johannes De Smedt; Pnina Soffer. Springer, 2024. p. 98-109 (Lecture Notes in Business Information Processing; Vol. 503 LNBIP).

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

TY - GEN

T1 - Turning Logs Into Lumber

T2 - Preprocessing Tasks in Process Mining

AU - Liu, Ying

AU - Stein Dani, Vinicius

AU - Beerepoot, Iris

AU - Lu, Xixi

PY - 2024/4/13

Y1 - 2024/4/13

N2 - Event logs are invaluable for conducting process mining projects, offering insights into process improvement and data-driven decision-making. However, data quality issues affect the correctness and trustworthiness of these insights, making preprocessing tasks a necessity. Despite the recognized importance, the execution of preprocessing tasks remains ad-hoc, lacking support. This paper presents a systematic literature review that establishes a comprehensive repository of preprocessing tasks and their usage in case studies. We identify six high-level and 20 low-level preprocessing tasks in case studies. Log filtering, transformation, and abstraction are commonly used, while log enriching, integration, and reduction are less frequent. These results can be considered a first step in contributing to more structured, transparent event log preprocessing, enhancing process mining reliability.

AB - Event logs are invaluable for conducting process mining projects, offering insights into process improvement and data-driven decision-making. However, data quality issues affect the correctness and trustworthiness of these insights, making preprocessing tasks a necessity. Despite the recognized importance, the execution of preprocessing tasks remains ad-hoc, lacking support. This paper presents a systematic literature review that establishes a comprehensive repository of preprocessing tasks and their usage in case studies. We identify six high-level and 20 low-level preprocessing tasks in case studies. Log filtering, transformation, and abstraction are commonly used, while log enriching, integration, and reduction are less frequent. These results can be considered a first step in contributing to more structured, transparent event log preprocessing, enhancing process mining reliability.

KW - Event log

KW - Log preprocessing

KW - Process mining

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DO - 10.1007/978-3-031-56107-8_8

M3 - Conference contribution

SN - 9783031561061

T3 - Lecture Notes in Business Information Processing

SP - 98

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BT - Process Mining Workshops - ICPM 2023 International Workshops, 2023, Revised Selected Papers

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Liu Y, Stein Dani V , Beerepoot I , Lu X. Turning Logs Into Lumber: Preprocessing Tasks in Process Mining. In De Smedt J, Soffer P, editors, Process Mining Workshops - ICPM 2023 International Workshops, 2023, Revised Selected Papers: The Event Data & Behavioral Analytics (EdbA 2023). Springer. 2024. p. 98-109. (Lecture Notes in Business Information Processing). doi: 10.1007/978-3-031-56107-8_8

Turning Logs Into Lumber: Preprocessing Tasks in Process Mining

Abstract

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