Online anomaly detection using statistical leverage for streaming business process events

• URL: http://arxiv.org/abs/2103.00831v1
• Date: Mon, 1 Mar 2021 08:01:49 GMT
• Title: Online anomaly detection using statistical leverage for streaming business process events
• Authors: Jonghyeon Ko and Marco Comuzzi
• Abstract summary: Event log anomaly detection in online settings can be crucial for discovering anomalies in process execution as soon as they occur. This paper describes a novel approach to event log anomaly detection on event streams that uses statistical leverage.
• Score: 4.9342793303029975
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: While several techniques for detecting trace-level anomalies in event logs in offline settings have appeared recently in the literature, such techniques are currently lacking for online settings. Event log anomaly detection in online settings can be crucial for discovering anomalies in process execution as soon as they occur and, consequently, allowing to promptly take early corrective actions. This paper describes a novel approach to event log anomaly detection on event streams that uses statistical leverage. Leverage has been used extensively in statistics to develop measures to identify outliers and it has been adapted in this paper to the specific scenario of event stream data. The proposed approach has been evaluated on both artificial and real event streams.

Related papers

• Event Detection via Probability Density Function Regression [0.0]
  This study introduces a generalized regression-based approach to reframe the time-interval-defined event detection problem. Inspired by heatmap regression techniques from computer vision, our approach aims to predict probability densities at event locations. We demonstrate that regression-based approaches outperform segmentation-based methods across various state-of-the-art baseline networks and datasets.
  arXiv  Detail & Related papers  (2024-08-23T01:58:56Z)
• Detecting Anomalous Events in Object-centric Business Processes via Graph Neural Networks [55.583478485027]
  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.
  arXiv  Detail & Related papers  (2024-02-14T14:17:56Z)
• A Critical Review of Common Log Data Sets Used for Evaluation of Sequence-based Anomaly Detection Techniques [2.5339493426758906]
  We analyze six publicly available log data sets with focus on the manifestations of anomalies and simple techniques for their detection. Our findings suggest that most anomalies are not directly related to sequential manifestations and that advanced detection techniques are not required to achieve high detection rates on these data sets.
  arXiv  Detail & Related papers  (2023-09-06T09:31:17Z)
• Real-Time Outlier Detection with Dynamic Process Limits [0.609170287691728]
  This paper proposes an online anomaly detection algorithm for existing real-time infrastructures. Online inverse cumulative distribution-based approach is introduced to eliminate common problems of offline anomaly detectors. The benefit of the proposed method is the ease of use, fast computation, and deployability as shown in two case studies of real microgrid operation data.
  arXiv  Detail & Related papers  (2023-01-31T10:23:02Z)
• PULL: Reactive Log Anomaly Detection Based On Iterative PU Learning [58.85063149619348]
  We propose PULL, an iterative log analysis method for reactive anomaly detection based on estimated failure time windows. Our evaluation shows that PULL consistently outperforms ten benchmark baselines across three different datasets.
  arXiv  Detail & Related papers  (2023-01-25T16:34:43Z)
• The Analysis of Online Event Streams: Predicting the Next Activity for Anomaly Detection [0.696125353550498]
  We propose to tackle the online event anomaly detection problem using next-activity prediction methods. We compare these predictive anomaly detection methods to four classical unsupervised anomaly detection approaches.
  arXiv  Detail & Related papers  (2022-03-17T21:17:19Z)
• A2Log: Attentive Augmented Log Anomaly Detection [53.06341151551106]
  Anomaly detection becomes increasingly important for the dependability and serviceability of IT services. Existing unsupervised methods need anomaly examples to obtain a suitable decision boundary. We develop A2Log, which is an unsupervised anomaly detection method consisting of two steps: Anomaly scoring and anomaly decision.
  arXiv  Detail & Related papers  (2021-09-20T13:40:21Z)
• Real-Time Anomaly Detection in Edge Streams [49.26098240310257]
  We propose MIDAS, which focuses on detecting microcluster anomalies, or suddenly arriving groups of suspiciously similar edges. We further propose MIDAS-F, to solve the problem by which anomalies are incorporated into the algorithm's internal states. Experiments show that MIDAS-F has significantly higher accuracy than MIDAS.
  arXiv  Detail & Related papers  (2020-09-17T17:59:27Z)
• 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)
• A Multi-Channel Neural Graphical Event Model with Negative Evidence [76.51278722190607]
  Event datasets are sequences of events of various types occurring irregularly over the time-line. We propose a non-parametric deep neural network approach in order to estimate the underlying intensity functions.
  arXiv  Detail & Related papers  (2020-02-21T23:10:50Z)

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.