Anomaly Detection in Log Streams based on Time-Contextual Models
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21240%2F25%3A00381387" target="_blank" >RIV/68407700:21240/25:00381387 - isvavai.cz</a>
Result on the web
<a href="https://link.springer.com/chapter/10.1007/978-981-96-0576-7_2" target="_blank" >https://link.springer.com/chapter/10.1007/978-981-96-0576-7_2</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1007/978-981-96-0576-7_2" target="_blank" >10.1007/978-981-96-0576-7_2</a>
Alternative languages
Result language
angličtina
Original language name
Anomaly Detection in Log Streams based on Time-Contextual Models
Original language description
Organisations today heavily rely on complex software systems integrated through multiple layers of middleware. This complexity leads to substantial generation of operational data of structured and semi-structured formats which is recorded in log files. The workload of the system fluctuates according to specific periods of the day which impacts the amount and quality of data generated in log files. In this paper, we propose a new log anomaly detection approach that leverages a collection of smaller models designed to capture workload fluctuations over specific time intervals. We demonstrate its effectiveness in detecting anomalies within log streams. Our evaluation uses log data from servers in a production environment, handling a complex back-end system that processes hundreds of requests per second. We show that our method outperforms traditional and widely used anomaly detection methods in data streams in the context of dynamic and time-sensitive workload scenarios.
Czech name
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Czech description
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Classification
Type
D - Article in proceedings
CEP classification
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OECD FORD branch
10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)
Result continuities
Project
—
Continuities
S - Specificky vyzkum na vysokych skolach
Others
Publication year
2025
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Article name in the collection
Web Information Systems Engineering – WISE 2024
ISBN
978-981-96-0575-0
ISSN
0302-9743
e-ISSN
1611-3349
Number of pages
11
Pages from-to
19-29
Publisher name
Springer Nature Singapore Pte Ltd.
Place of publication
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Event location
Doha
Event date
Dec 2, 2024
Type of event by nationality
WRD - Celosvětová akce
UT code for WoS article
001534861200002