Anomaly monitoring framework based on intelligent data analysis

Prapa Rattadilok, Andrei Petrovski, Sergei Petrovski

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

7 Citations (Scopus)

Abstract

Real-time data processing has become an increasingly important challenge as the need for faster analysis of big data widely manifests itself. In this research, several Computational Intelligence methods have been applied for identifying possible anomalies in two real world sensor-based datasets. By achieving similar results to those of well respected methods, the proposed framework shows a promising potential for anomaly detection and its lightweight, real-time features make it applicable to a range of in-situ data analysis scenarios.

Original languageEnglish
Title of host publicationIntelligent Data Engineering and Automated Learning - 14th International Conference, IDEAL 2013, Proceedings
Pages134-141
Number of pages8
DOIs
Publication statusPublished - 2013
Externally publishedYes
Event14th International Conference on Intelligent Data Engineering and Automated Learning, IDEAL 2013 - Hefei, China
Duration: 20 Oct 201323 Oct 2013

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume8206 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference14th International Conference on Intelligent Data Engineering and Automated Learning, IDEAL 2013
Country/TerritoryChina
CityHefei
Period20/10/1323/10/13

Keywords

  • K-Means
  • automated fault detection
  • big data
  • intelligent data analysis
  • real-time

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science (all)

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