Autoencoder-Based Detection of Delays, Handovers and Workloads over High-Level Events

Irne Verwijst, Robin Mennens, Roeland Scheepens, Marwan Hassani (Corresponding author)

Research output: Chapter in Book/Report/Conference proceedingConference contributionAcademicpeer-review

Abstract

Detecting delays, anomalous work handovers, and high workloads is a challenging process mining task that is typically performed at the case level. However, process mining users would benefit from analyzing such behaviors at the process level where instances of such behavior are called high-level events. We propose a novel framework for high-level event mining that leverages anomaly detection and clustering methods to identify and analyze high-level events in an unsupervised setting. Our framework, called High-level Event Mining Machine Learning Approach (HEMMLA), utilizes an autoencoder-based anomaly detection method and requires no predefined time window or anomaly thresholds. An extensive experimental evaluation over real and synthetic datasets highlights the high scalability of our approach. An additional user study over real datasets underlines the ability of our framework to detect more interesting and explainable anomalies than the state-of-the-art.
Original languageEnglish
Title of host publicationCooperative Information Systems
Subtitle of host publication30th International Conference, CoopIS 2024, Porto, Portugal, November 19–21, 2024, Proceedings
EditorsMarco Comuzzi, Daniela Grigori, Mohamed Sellami, Zhangbing Zhou
Place of PublicationCham
PublisherSpringer
Pages111-128
Number of pages18
ISBN (Electronic)978-3-031-81375-7
ISBN (Print)978-3-031-81374-0
DOIs
Publication statusPublished - 14 Feb 2025
Event30th International Conference on Cooperative Information Systems - Vila Gale Hotel, Porto, Portugal
Duration: 19 Nov 202421 Nov 2024
Conference number: 30
https://coopis.scitevents.org/

Publication series

NameLecture Notes in Computer Science (LNCS)
Volume15506
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference30th International Conference on Cooperative Information Systems
Abbreviated titleCoopIS 2024
Country/TerritoryPortugal
CityPorto
Period19/11/2421/11/24
Internet address

Keywords

  • Anomaly detection
  • Clustering
  • Dynamic process behavior
  • High-level events
  • Process mining

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