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Semi-Supervised Detection of Contaminated Business Process Instances Using Graph
Autoencoders and Dynamic Edge Convolutions for BPM Anomaly Detection | IEEE
Conference Publication | IEEE Xplore
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Conferences >2024 Innovations in Intellige...
SEMI-SUPERVISED DETECTION OF CONTAMINATED BUSINESS PROCESS INSTANCES USING GRAPH
AUTOENCODERS AND DYNAMIC EDGE CONVOLUTIONS FOR BPM ANOMALY DETECTION
Publisher: IEEE
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Teoman Berkay Ayaz; Ege Gülce; Alper Özcan; Akhan Akbulut
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Abstract
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Introduction
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ABSTRACT:
Anomalies in business processes pose a significant threat to the operational
performance of modern enterprises. Consequently, detecting these anomalies in
business proces...View more
METADATA
ABSTRACT:
Anomalies in business processes pose a significant threat to the operational
performance of modern enterprises. Consequently, detecting these anomalies in
business process management (BPM) systems is a critical task for organizations.
However, the inherent complexity of business processes and the limited
availability of labeled data compound this challenge. In this study, we
introduce innovative models that leverage graph representations of business
process data, incorporating an unsupervised neural network architecture known as
Graph Autoencoders combined with Dynamic Edge Conditioned Convolutions to
enhance learning on edge attributes. Additionally, we propose a novel metric for
evaluating autoencoder performance. We developed two models: a fully
unsupervised model trained on the entire dataset and a semi-supervised model
that refines the initial model's outputs. Our empirical results demonstrate that
the unsupervised model achieves a maximum F1-score of 0.82 and a success rate of
74.11% at the 80th percentile threshold for detecting anomalous sub-sequences in
business processes. The semi-supervised model, which builds on the unsupervised
model's findings, achieves a maximum F1-score of 0.89 and a success rate of
80.50%, indicating a substantial performance improvement. Both models offer
promising capabilities for automating anomaly detection in BPM systems.
Published in: 2024 Innovations in Intelligent Systems and Applications
Conference (ASYU)
Date of Conference: 16-18 October 2024
Date Added to IEEE Xplore: 28 November 2024
ISBN Information:
ISSN INFORMATION:
DOI: 10.1109/ASYU62119.2024.10757160
Publisher: IEEE
Conference Location: Ankara, Turkiye
FUNDING AGENCY:
Contents
--------------------------------------------------------------------------------
I. INTRODUCTION
Business Process Management (BPM) plays an important role in the modern world as
it involves the structured design, execution, monitoring, and optimization of
business processes. Successful BPM empowers organizations to improve operational
productivity and achieve strategic goals. By evaluation of process data, BPM
systems provide insights into workflow performance and pinpoint areas for
enhancement. Business process anomalies refer to deviations from the expected
process behavior, signaling potential inefficiencies, errors, or fraudulent
behaviors. These anomalies can disrupt regular operations, resulting in higher
costs, lower productivity, and decreased efficiency. Therefore, identifying and
addressing anomalies is crucial for maintaining the integrity and efficiency of
business processes. Anomaly detection in this context can assist in the
correction of said deviations, increasing the efficiency and preventing
potential fraudulent activity in turn increasing the profitability of the BPM
tenant.
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2024 9th International Conference on Computer Science and Engineering (UBMK)
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