Subject: AIOps, logs, graphs, visualization, software engineering, design patterns
Year: 2022
Type: Proceedings
Title: logs2graphs: Data-driven graph representation and visualization of log data
Author: Andonov, Stefan
Author: Jovev, Viktor
Author: Kitanovski, Aleksandar
Author: Krsteski, Aleksandar
Author: Madjarov, Gjorgji
Abstract: In recent years, AIOps has helped a lot with the exploration of different types of resources, in the processes of optimization and automation of complex IT operations. One of the main resources that AIOps is exploring is system logs. There are many techniques based on machine learning in AIOps that help in logs anomaly detection, logs prediction, and root cause analysis guided by logs, but a majority of them are considering log messages either individually or as log sequences, without exploring the relationships between different types of logs. We believe that those relationships can be expressed via using graph representations of log messages and those representations can be utilized in almost any AIOps operation. Therefore in this paper, we present logs2graphs, an open-source system for the creation and visualization of such graph representations of log messages, which is compatible with several publicly available log sources and expandable to other log sources.
Publisher:
Relation: The 19th International Conference on Informatics and Information Technologies – CIIT 2022
Identifier: oai:repository.ukim.mk:20.500.12188/25690
Identifier: http://hdl.handle.net/20.500.12188/25690
