An integrated framework on mining logs files for computing system management

Tao Li, Sheng Ma, Feng Liang, Wei Peng

Research output: Contribution to conferencePaper

Abstract

Traditional approaches to system management have been largely based on domain experts through a knowledge acquisition process that translates domain knowledge into operating rules and policies. This has been well known and experienced as a cumbersome, labor intensive, and error prone process. In addition, this process is difficult to keep up with the rapidly changing environments. In this paper, we will describe our research efforts on establishing an integrated framework for mining system log files for automatic management. In particular, we apply text mining techniques to categorize messages in log files into common situations, improve categorization accuracy by considering the temporal characteristics of log messages, develop temporal mining techniques to discover the relationships between different events, and utilize visualization tools to evaluate and validate the interesting temporal patterns for system management.

Original languageEnglish (US)
Pages776-781
Number of pages6
StatePublished - Dec 1 2005
Externally publishedYes
EventKDD-2005: 11th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining - Chicago, IL, United States
Duration: Aug 21 2005Aug 24 2005

Other

OtherKDD-2005: 11th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining
CountryUnited States
CityChicago, IL
Period8/21/058/24/05

Keywords

  • Event Relationship
  • Log Categorization
  • System Management
  • Temporal Pattern

ASJC Scopus subject areas

  • Software
  • Information Systems

Fingerprint Dive into the research topics of 'An integrated framework on mining logs files for computing system management'. Together they form a unique fingerprint.

  • Cite this

    Li, T., Ma, S., Liang, F., & Peng, W. (2005). An integrated framework on mining logs files for computing system management. 776-781. Paper presented at KDD-2005: 11th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, Chicago, IL, United States.