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Check my flow: distributed conformance checking at the source
Publikationstyp
Book Part
Date Issued
2025-02-20
Sprache
English
Author(s)
First published in
Number in series
534
Start Page
101
End Page
112
Citation
Lecture notes in business information processing 534: 101–112 (2025)
Contribution to Conference
Publisher DOI
Publisher
Springer Nature Switzerland
ISBN
978-3-031-78665-5
978-3-031-78666-2
IoT networks in, for example, smart manufacturing, smart homes, and smart health demand process mining beyond traditional event logs. However, conventional process discovery and conformance checking algorithms expect data to be collected at a central location for mining. As a result, they struggle to handle the vast amounts of data generated by IoT networks, which often leads to privacy concerns. To address these challenges, this paper introduces Distributed Conformance Checking (DisCC). DisCC leverages a footprint-fitness method to perform distributed online conformance checks directly at the data source where an event is sensed, ensuring scalable conformance checking and enabling privacy by only sharing aggregated event data with a central entity. Our evaluation of DisCC demonstrates its effectiveness in accurately performing conformance checks at the event, trace, and log levels. We experimentally show the correctness of DisCC and how quickly its interim results converge to the correct fitness value. The system supports real-time alerts for non-conforming events and traces, detects concept drifts and temporary fitness losses through a sliding window implementation, and offers a scalable, privacy-enabling solution for
process monitoring in IoT networks.
process monitoring in IoT networks.
Subjects
Online Conformance Checking
Distributed Process Mining
Event Data Stream
Scalability
DDC Class
600: Technology