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Anomaly monitoring framework based on intelligent data analysis.

Rattadilok, Prapa; Petrovski, Andrei; Petrovski, Sergei

Authors

Prapa Rattadilok

Sergei Petrovski



Contributors

Hujun Yin
Editor

Ke Tang
Editor

Yang Gao
Editor

Frank Klawonn
Editor

Minho Lee
Editor

Thomas Weise
Editor

Xin Yao
Editor

Bin Li
Editor

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 methods of Computational Intelligence have been applied for identifying possible anomalies in two datasets. 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.

Citation

RATTADILOK, P., PETROVSKI, A. and PETROVSKI, S. 2013. Anomaly monitoring framework based on intelligent data analysis. In Yin, H., Tang, K., Gao, Y., Klawonn, F., Lee, M., Weise, T., Li, B. and Yao, X. (eds.) Proceedings of the 14th International conference on intelligent data engineering and automated learning (IDEAL 2013), 20-23 October 2013, Hefei, China. Lecture notes in computer science, 8206. Berlin: Springer [online], pages 134-141. Available from: https://doi.org/10.1007/978-3-642-41278-3_17

Conference Name 14th International conference on intelligent data engineering and automated learning (IDEAL 2013)
Conference Location Hefei, China
Start Date Oct 20, 2013
End Date Oct 23, 2013
Acceptance Date Oct 31, 2013
Online Publication Date Oct 31, 2013
Publication Date Oct 31, 2013
Deposit Date Feb 13, 2015
Publicly Available Date Feb 13, 2015
Print ISSN 0302-9743
Publisher Springer
Pages 134-141
Series Title Lecture notes in computer science
Series Number 8206
Series ISSN 0302-9743
ISBN 9783642412783
DOI https://doi.org/10.1007/978-3-642-41278-3_17
Keywords Intelligent data analysis; Automated fault detection; Big data
Public URL http://hdl.handle.net/10059/1146

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