Anomaly monitoring framework based on intelligent data analysis.

Rattadilok, Prapa; Petrovski, Andrei; Petrovski, Sergei

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.

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