Detecting Anomalous Events in Object-centric Business Processes via Graph Neural Networks

• URL: http://arxiv.org/abs/2403.00775v1
• Date: Wed, 14 Feb 2024 14:17:56 GMT
• Title: Detecting Anomalous Events in Object-centric Business Processes via Graph Neural Networks
• Authors: Alessandro Niro and Michael Werner
• Abstract summary: This study proposes a novel framework for anomaly detection in business processes. We first reconstruct the process dependencies of the object-centric event logs as attributed graphs. We then employ a graph convolutional autoencoder architecture to detect anomalous events.
• Score: 55.583478485027
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Detecting anomalies is important for identifying inefficiencies, errors, or fraud in business processes. Traditional process mining approaches focus on analyzing 'flattened', sequential, event logs based on a single case notion. However, many real-world process executions exhibit a graph-like structure, where events can be associated with multiple cases. Flattening event logs requires selecting a single case identifier which creates a gap with the real event data and artificially introduces anomalies in the event logs. Object-centric process mining avoids these limitations by allowing events to be related to different cases. This study proposes a novel framework for anomaly detection in business processes that exploits graph neural networks and the enhanced information offered by object-centric process mining. We first reconstruct and represent the process dependencies of the object-centric event logs as attributed graphs and then employ a graph convolutional autoencoder architecture to detect anomalous events. Our results show that our approach provides promising performance in detecting anomalies at the activity type and attributes level, although it struggles to detect anomalies in the temporal order of events.

Related papers

• GLAD: Content-aware Dynamic Graphs For Log Anomaly Detection [49.9884374409624]
  GLAD is a Graph-based Log Anomaly Detection framework designed to detect anomalies in system logs. We introduce GLAD, a Graph-based Log Anomaly Detection framework designed to detect anomalies in system logs.
  arXiv  Detail & Related papers  (2023-09-12T04:21:30Z)
• Graph-based Asynchronous Event Processing for Rapid Object Recognition [59.112755601918074]
  Event cameras capture asynchronous events stream in which each event encodes pixel location, trigger time, and the polarity of the brightness changes. We introduce a novel graph-based framework for event cameras, namely SlideGCN. Our approach can efficiently process data event-by-event, unlock the low latency nature of events data while still maintaining the graph's structure internally.
  arXiv  Detail & Related papers  (2023-08-28T08:59:57Z)
• Abnormal Event Detection via Hypergraph Contrastive Learning [54.80429341415227]
  Abnormal event detection plays an important role in many real applications. In this paper, we study the unsupervised abnormal event detection problem in Attributed Heterogeneous Information Network. A novel hypergraph contrastive learning method, named AEHCL, is proposed to fully capture abnormal event patterns.
  arXiv  Detail & Related papers  (2023-04-02T08:23:20Z)
• Avoiding Post-Processing with Event-Based Detection in Biomedical Signals [69.34035527763916]
  We propose an event-based modeling framework that directly works with events as learning targets. We show that event-based modeling (without post-processing) performs on par with or better than epoch-based modeling with extensive post-processing.
  arXiv  Detail & Related papers  (2022-09-22T13:44:13Z)
• Defining Cases and Variants for Object-Centric Event Data [0.36748639131154304]
  We introduce the case concept for object-centric process mining: process executions. We provide techniques to extract process executions. We show the most frequent object-centric variants of a real-life event log.
  arXiv  Detail & Related papers  (2022-08-05T15:33:03Z)
• Event Detection on Dynamic Graphs [4.128347119808724]
  Event detection is a critical task for timely decision-making in graph analytics applications. We propose DyGED, a simple yet novel deep learning model for event detection on dynamic graphs.
  arXiv  Detail & Related papers  (2021-10-23T05:52:03Z)
• Anomaly Detection for Aggregated Data Using Multi-Graph Autoencoder [21.81622481466591]
  We focus on creating an Anomaly detection models for system logs. We present a thorough analysis of the aggregated data and the relationships between aggregated events. We propose Multiple-graphs autoencoder MGAE, a novel convolutional graphs-autoencoder model.
  arXiv  Detail & Related papers  (2021-01-11T17:38:42Z)
• Conformance Checking over Uncertain Event Data [0.45119235878273]
  We analyze the previously unexplored setting of uncertain event logs. In such event logs uncertainty is recorded explicitly, i.e., the time, activity and case of an event may be unclear or imprecise. We show how upper and lower bounds for conformance can be obtained by aligning an uncertain trace onto a regular process model.
  arXiv  Detail & Related papers  (2020-09-29T14:27:30Z)
• A Background-Agnostic Framework with Adversarial Training for Abnormal Event Detection in Video [120.18562044084678]
  Abnormal event detection in video is a complex computer vision problem that has attracted significant attention in recent years. We propose a background-agnostic framework that learns from training videos containing only normal events.
  arXiv  Detail & Related papers  (2020-08-27T18:39:24Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.