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 language | English |
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Title of host publication | Cooperative Information Systems |
Subtitle of host publication | 30th International Conference, CoopIS 2024, Porto, Portugal, November 19–21, 2024, Proceedings |
Editors | Marco Comuzzi, Daniela Grigori, Mohamed Sellami, Zhangbing Zhou |
Place of Publication | Cham |
Publisher | Springer |
Pages | 111-128 |
Number of pages | 18 |
ISBN (Electronic) | 978-3-031-81375-7 |
ISBN (Print) | 978-3-031-81374-0 |
DOIs | |
Publication status | Published - 14 Feb 2025 |
Event | 30th International Conference on Cooperative Information Systems - Vila Gale Hotel, Porto, Portugal Duration: 19 Nov 2024 → 21 Nov 2024 Conference number: 30 https://coopis.scitevents.org/ |
Publication series
Name | Lecture Notes in Computer Science (LNCS) |
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Volume | 15506 |
ISSN (Print) | 0302-9743 |
ISSN (Electronic) | 1611-3349 |
Conference
Conference | 30th International Conference on Cooperative Information Systems |
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Abbreviated title | CoopIS 2024 |
Country/Territory | Portugal |
City | Porto |
Period | 19/11/24 → 21/11/24 |
Internet address |
Keywords
- Anomaly detection
- Clustering
- Dynamic process behavior
- High-level events
- Process mining