Explainable Log Parsing and Online Interval Granular Classification from Streams of Words

Leticia Decker, Daniel Leite, Daniele Bonacorsi

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

We introduce a method called evolving Log Parsing (eLP) to extract information granules and an interval rule-based classification model from streams of words in unstructured log files. Logs are elementary expressions of language that are used by computational systems to communicate with humans unidirectionally. The logs tell stories based on event occurrences. Any software expresses itself through a log language. In particular, the eLP approach has identified templates (patterns in textual data) in an unsupervised and incremental way. Online pattern classification is achieved with effectiveness of (96.05 ± 1.04)% using 6 datasets and eLP models exhibiting an interpretability level of about 0.04. We present a recursive model-interpretability index to evaluate rule-based classifiers, and discuss the effectiveness-interpretability tradeoff on an actual scenario, namely, the StorM Service of a computing center.

Original languageEnglish
Title of host publication2022 IEEE International Conference on Fuzzy Systems, FUZZ 2022 - Proceedings
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781665467100
DOIs
StatePublished - 2022
Externally publishedYes
Event2022 IEEE International Conference on Fuzzy Systems, FUZZ 2022 - Padua, Italy
Duration: 18 Jul 202223 Jul 2022

Publication series

NameIEEE International Conference on Fuzzy Systems
Volume2022-July
ISSN (Print)1098-7584

Conference

Conference2022 IEEE International Conference on Fuzzy Systems, FUZZ 2022
Country/TerritoryItaly
CityPadua
Period18/07/2223/07/22

Keywords

  • Log parsing
  • granular computing
  • interval mathematics
  • online machine learning
  • predictive maintenance

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