Predictive diagnostics of computer systems logs using natural language processing techniques

This study aims to develop and validate a method for predictive diagnostics and anomaly detection in computer system logs, using the Vertica database as a case study. The proposed approach is based on semisupervised learning combined with natural language processing techniques. A specialized parser...

Ausführliche Beschreibung

Gespeichert in:
Bibliographische Detailangaben
Veröffentlicht in:Discrete and continuous models and applied computational science Jg. 33; H. 2; S. 172 - 183
Hauptverfasser: Kiriachek, Vladislav A., Salpagarov, Soltan I.
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Peoples’ Friendship University of Russia (RUDN University) 15.07.2025
Schlagworte:
anomaly detection
log analysis
machine learning
natural language processing
predictive diagnostics
ISSN:2658-4670, 2658-7149
Online-Zugang:Volltext
Tags: Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
Beschreibung
Zusammenfassung:This study aims to develop and validate a method for predictive diagnostics and anomaly detection in computer system logs, using the Vertica database as a case study. The proposed approach is based on semisupervised learning combined with natural language processing techniques. A specialized parser utilizing a semantic graph was developed for data preprocessing. Vectorization was performed using the fastText NLP library and TF-IDF weighting. Empirical validation was conducted on real Vertica log files from a large IT company, containing periods of normal operation and anomalies leading to failures. A comparative assessment of various anomaly detection algorithms was performed, including k-nearest neighbors, autoencoders, One Class SVM, Isolation Forest, Local Outlier Factor, and Elliptic Envelope. Results are visualized through anomaly graphs depicting time intervals exceeding the threshold level. The findings demonstrate high efficacy of the proposed approach in identifying anomalies preceding system failures and delineate promising directions for further research.
ISSN:2658-4670
2658-7149
DOI:10.22363/2658-4670-2025-33-2-172-183