Rough Logs: A Data Reduction Approach for Log Files

Meinig, Michael; Tröger, Peter; Meinel, Christoph

doi:10.5220/0007735102950302

Cited by 2 publications

(1 citation statement)

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“…Particularly, the method first identified frequently occurring patterns from dense log data byfrequent pattern mining, and then linked patterns to the data as a data directory. Meinig et al [104] proposed an approach adopted from the rough set in the uncertainty theory. It treated the log file as a decision table with removable attributes identified by an one-time analysis of log data.…”

Section: Statistics-based Compressionmentioning

confidence: 99%

A Survey on Automated Log Analysis for Reliability Engineering

Chen

et al. 2020

Preprint

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Logs are semi-structured text generated by logging statements in software source code. In recent decades, software logs have become imperative in the reliability assurance mechanism of many software systems because they are often the only data available that record software runtime information. As modern software is evolving into a large scale, the volume of logs has increased rapidly. To enable effective and efficient usage of modern software logs in reliability engineering, a number of studies have been conducted on automated log analysis. This survey presents a detailed overview of automated log analysis research, including how to automate and assist the writing of logging statements, how to compress logs, how to parse logs into structured event templates, and how to employ logs to detect anomalies, predict failures, and facilitate diagnosis. Additionally, we survey work that releases open-source toolkits and datasets. Based on the discussion of the recent advances, we present several promising future directions toward real-world and next-generation automated log analysis.CCS Concepts: • Software and its engineering → Software maintenance tools; Software creation and management.

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Section: Statistics-based Compressionmentioning

confidence: 99%

A Survey on Automated Log Analysis for Reliability Engineering

Chen

et al. 2020

Preprint

View full text Add to dashboard Cite

show abstract

A Survey on Automated Log Analysis for Reliability Engineering

Chen

et al. 2021

ACM Comput. Surv.

100

View full text Add to dashboard Cite

Logs are semi-structured text generated by logging statements in software source code. In recent decades, software logs have become imperative in the reliability assurance mechanism of many software systems, because they are often the only data available that record software runtime information. As modern software is evolving into a large scale, the volume of logs has increased rapidly. To enable effective and efficient usage of modern software logs in reliability engineering, a number of studies have been conducted on automated log analysis. This survey presents a detailed overview of automated log analysis research, including how to automate and assist the writing of logging statements, how to compress logs, how to parse logs into structured event templates, and how to employ logs to detect anomalies, predict failures, and facilitate diagnosis. Additionally, we survey work that releases open-source toolkits and datasets. Based on the discussion of the recent advances, we present several promising future directions toward real-world and next-generation automated log analysis.

show abstract

Rough Logs: A Data Reduction Approach for Log Files

Cited by 2 publications

References 0 publications

A Survey on Automated Log Analysis for Reliability Engineering

A Survey on Automated Log Analysis for Reliability Engineering

A Survey on Automated Log Analysis for Reliability Engineering

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