Scalable temporal anomaly causality discovery in large systems: achieving computational efficiency with binary anomaly flag data.
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| Title: | Scalable temporal anomaly causality discovery in large systems: achieving computational efficiency with binary anomaly flag data. |
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| Authors: | Asres, Mulugeta Weldezgina1 (AUTHOR) mulugetawa@uia.no, Omlin, Christian Walter1 (AUTHOR) christian.omlin@uia.no |
| Source: | European Physical Journal C -- Particles & Fields. May2026, Vol. 86 Issue 5, p1-26. 26p. |
| Subjects: | Causal models, Large scale systems, Causal inference, Optimization algorithms, Sensor networks |
| Abstract: | Extracting anomaly causality facilitates diagnostics once monitoring systems detect system faults. Identifying anomaly causes in large systems involves investigating a broader set of monitoring variables across multiple subsystems. However, learning graphical causal models (GCMs) comes with a significant computational burden that restrains the applicability of most existing methods in real-time and large-scale deployments. In addition, modern monitoring applications for large systems often generate large amounts of binary alarm flags, and the distinct characteristics of binary anomaly data—the meaning of state transition and data sparsity—challenge existing causality learning mechanisms. This study proposes an anomaly causal discovery approach (AnomalyCD), addressing the accuracy and computational challenges of generating GCMs from temporal binary flag datasets. The AnomalyCD presents several strategies, such as anomaly data-aware causality testing, sparse data and prior link compression, and edge pruning adjustment approaches. We validate the performance of the approach on two datasets: monitoring sensor data from the readout-box system of the Compact Muon Solenoid experiment at CERN, and a public dataset from an information technology monitoring system. The results on temporal GCMs demonstrate a considerable reduction of computation overhead and a moderate enhancement of accuracy on the binary anomaly datasets. Source code: https://github.com/muleina/AnomalyCD [ABSTRACT FROM AUTHOR] |
| Copyright of European Physical Journal C -- Particles & Fields is the property of Springer Nature and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
| Database: | Engineering Source |
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| Header | DbId: egs DbLabel: Engineering Source An: 194633844 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: Scalable temporal anomaly causality discovery in large systems: achieving computational efficiency with binary anomaly flag data. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Asres%2C+Mulugeta+Weldezgina%22">Asres, Mulugeta Weldezgina</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> mulugetawa@uia.no</i><br /><searchLink fieldCode="AR" term="%22Omlin%2C+Christian+Walter%22">Omlin, Christian Walter</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> christian.omlin@uia.no</i> – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22European+Physical+Journal+C+--+Particles+%26+Fields%22">European Physical Journal C -- Particles & Fields</searchLink>. May2026, Vol. 86 Issue 5, p1-26. 26p. – Name: Subject Label: Subjects Group: Su Data: <searchLink fieldCode="DE" term="%22Causal+models%22">Causal models</searchLink><br /><searchLink fieldCode="DE" term="%22Large+scale+systems%22">Large scale systems</searchLink><br /><searchLink fieldCode="DE" term="%22Causal+inference%22">Causal inference</searchLink><br /><searchLink fieldCode="DE" term="%22Optimization+algorithms%22">Optimization algorithms</searchLink><br /><searchLink fieldCode="DE" term="%22Sensor+networks%22">Sensor networks</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: Extracting anomaly causality facilitates diagnostics once monitoring systems detect system faults. Identifying anomaly causes in large systems involves investigating a broader set of monitoring variables across multiple subsystems. However, learning graphical causal models (GCMs) comes with a significant computational burden that restrains the applicability of most existing methods in real-time and large-scale deployments. In addition, modern monitoring applications for large systems often generate large amounts of binary alarm flags, and the distinct characteristics of binary anomaly data—the meaning of state transition and data sparsity—challenge existing causality learning mechanisms. This study proposes an anomaly causal discovery approach (AnomalyCD), addressing the accuracy and computational challenges of generating GCMs from temporal binary flag datasets. The AnomalyCD presents several strategies, such as anomaly data-aware causality testing, sparse data and prior link compression, and edge pruning adjustment approaches. We validate the performance of the approach on two datasets: monitoring sensor data from the readout-box system of the Compact Muon Solenoid experiment at CERN, and a public dataset from an information technology monitoring system. The results on temporal GCMs demonstrate a considerable reduction of computation overhead and a moderate enhancement of accuracy on the binary anomaly datasets. Source code: https://github.com/muleina/AnomalyCD [ABSTRACT FROM AUTHOR] – Name: AbstractSuppliedCopyright Label: Group: Ab Data: <i>Copyright of European Physical Journal C -- Particles & Fields is the property of Springer Nature and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.) |
| PLink | https://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=egs&AN=194633844 |
| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.1140/epjc/s10052-026-15611-5 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 26 StartPage: 1 Subjects: – SubjectFull: Causal models Type: general – SubjectFull: Large scale systems Type: general – SubjectFull: Causal inference Type: general – SubjectFull: Optimization algorithms Type: general – SubjectFull: Sensor networks Type: general Titles: – TitleFull: Scalable temporal anomaly causality discovery in large systems: achieving computational efficiency with binary anomaly flag data. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Asres, Mulugeta Weldezgina – PersonEntity: Name: NameFull: Omlin, Christian Walter IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 05 Text: May2026 Type: published Y: 2026 Identifiers: – Type: issn-print Value: 14346044 Numbering: – Type: volume Value: 86 – Type: issue Value: 5 Titles: – TitleFull: European Physical Journal C -- Particles & Fields Type: main |
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