research-article

Event-log abstraction using batch session identification and clustering

Authors:

Massimiliano de Leoni,

Safa DündarAuthors Info & Claims

SAC '20: Proceedings of the 35th Annual ACM Symposium on Applied Computing

Pages 36 - 44

https://doi.org/10.1145/3341105.3373861

Published: 30 March 2020 Publication History

Abstract

Process-Mining techniques aim to use event data about past executions to gain insight into how processes are executed. While these techniques are proven to be very valuable, they are less successful to reach their goal if the process is flexible and, hence, it exhibits an extremely large number of variants. Furthermore, information systems can record events at very low level, which do not match the high-level concepts known at business level. Without abstracting sequences of events to high-level concepts, the results of applying process mining (to, e.g., discover a model) easily become very complex and difficult to interpret, which ultimately means that they are of little use. A large body of research exists on event abstraction but typically a large amount of domain knowledge is required, which is often not readily available. Other abstraction techniques are unsupervised, which ultimately return less accurate results and/or rely on stronger assumptions. This paper puts forward a technique that requires limited domain knowledge that can be easily provided. Traces are divided in batch sessions, and each session is abstracted as one single high-level activity execution. The abstraction is based on a combination of automatic clustering and visualization methods. The technique was assessed on two case studies about processes characterized by high variability. The results clearly illustrate the benefits of the abstraction to convey accurate knowledge to stakeholders.

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Cited By

He JFang WLan XYang GChen ZChen YLi TChen J(2024)Efficient Based on Improved Random Forest Defense System Against Application‐Layer DDoS AttacksInternational Journal of Intelligent Systems10.1155/2024/90443912024:1Online publication date: Nov-2024
https://doi.org/10.1155/2024/9044391
Van Houdt Gde Leoni MMartin NDepaire B(2024)An empirical evaluation of unsupervised event log abstraction techniques in process miningInformation Systems10.1016/j.is.2023.102320121(102320)Online publication date: Mar-2024
https://doi.org/10.1016/j.is.2023.102320
Dogan Ode Leoni M(2024)Parallelism-Based Session Creation to Identify High-Level Activities in Event Log AbstractionProcess Mining Workshops10.1007/978-3-031-56107-8_5(58-69)Online publication date: 13-Apr-2024
https://doi.org/10.1007/978-3-031-56107-8_5
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cover image ACM Conferences

SAC '20: Proceedings of the 35th Annual ACM Symposium on Applied Computing

March 2020

2348 pages

ISBN:9781450368667

DOI:10.1145/3341105

Conference Chairs:
Chih-Cheng Hung
Kennesaw State University
,
Tomas Cerny
Baylor University
,
Program Chairs:
Dongwan Shin
New Mexico Tech
,
Alessio Bechini
University of Pisa, Italy

Copyright © 2020 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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SIGAPP: ACM Special Interest Group on Applied Computing

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Published: 30 March 2020

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SAC '20: The 35th ACM/SIGAPP Symposium on Applied Computing

March 30 - April 3, 2020

Brno, Czech Republic

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Overall Acceptance Rate 1,650 of 6,669 submissions, 25%

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Cited By

He JFang WLan XYang GChen ZChen YLi TChen J(2024)Efficient Based on Improved Random Forest Defense System Against Application‐Layer DDoS AttacksInternational Journal of Intelligent Systems10.1155/2024/90443912024:1Online publication date: Nov-2024
https://doi.org/10.1155/2024/9044391
Van Houdt Gde Leoni MMartin NDepaire B(2024)An empirical evaluation of unsupervised event log abstraction techniques in process miningInformation Systems10.1016/j.is.2023.102320121(102320)Online publication date: Mar-2024
https://doi.org/10.1016/j.is.2023.102320
Dogan Ode Leoni M(2024)Parallelism-Based Session Creation to Identify High-Level Activities in Event Log AbstractionProcess Mining Workshops10.1007/978-3-031-56107-8_5(58-69)Online publication date: 13-Apr-2024
https://doi.org/10.1007/978-3-031-56107-8_5
Lim JSong M(2024)Navigating Event Abstraction in Process Mining: A Comprehensive Analysis of Sub-problems, Data, and Process Characteristic ConsiderationsBusiness Process Management Workshops10.1007/978-3-031-50974-2_14(174-185)Online publication date: 11-Jan-2024
https://doi.org/10.1007/978-3-031-50974-2_14
Li Cvan Zelst Svan der Aalst W(2023)Event Abstraction for Partial Order PatternsBusiness Process Management10.1007/978-3-031-41620-0_3(38-54)Online publication date: 1-Sep-2023
https://doi.org/10.1007/978-3-031-41620-0_3
Li Cvan Zelst Svan der Aalst W(2023)A Framework for Automated Abstraction Class Detection for Event AbstractionIntelligent Systems Design and Applications10.1007/978-3-031-35507-3_13(126-136)Online publication date: 3-Jun-2023
https://doi.org/10.1007/978-3-031-35507-3_13
Hidalgo LMunoz-Gama J(2023)Domain-Driven Event Abstraction Framework for Learning Dynamics in MOOCs SessionsProcess Mining Workshops10.1007/978-3-031-27815-0_40(552-564)Online publication date: 26-Mar-2023
https://doi.org/10.1007/978-3-031-27815-0_40
Klijn EMannhardt FFahland D(2023)Aggregating Event Knowledge Graphs for Task AnalysisProcess Mining Workshops10.1007/978-3-031-27815-0_36(493-505)Online publication date: 26-Mar-2023
https://doi.org/10.1007/978-3-031-27815-0_36
Rebmann APfeiffer PFettke PAa H(2023)Multi-perspective Identification of Event Groups for Event AbstractionProcess Mining Workshops10.1007/978-3-031-27815-0_3(31-43)Online publication date: 26-Mar-2023
https://doi.org/10.1007/978-3-031-27815-0_3
Di Federico GBurattin A(2023)vAMoS: eVent Abstraction via Motifs SearchBusiness Process Management Workshops10.1007/978-3-031-25383-6_9(101-112)Online publication date: 9-Feb-2023
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