TY - GEN
T1 - Anomaly monitoring framework based on intelligent data analysis
AU - Rattadilok, Prapa
AU - Petrovski, Andrei
AU - Petrovski, Sergei
PY - 2013
Y1 - 2013
N2 - 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.
AB - 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.
KW - K-Means
KW - automated fault detection
KW - big data
KW - intelligent data analysis
KW - real-time
UR - http://www.scopus.com/inward/record.url?scp=84890873738&partnerID=8YFLogxK
U2 - 10.1007/978-3-642-41278-3_17
DO - 10.1007/978-3-642-41278-3_17
M3 - Conference contribution
AN - SCOPUS:84890873738
SN - 9783642412776
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 134
EP - 141
BT - Intelligent Data Engineering and Automated Learning - 14th International Conference, IDEAL 2013, Proceedings
T2 - 14th International Conference on Intelligent Data Engineering and Automated Learning, IDEAL 2013
Y2 - 20 October 2013 through 23 October 2013
ER -