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. © 2013 Springer-Verlag.
CITATION STYLE
Rattadilok, P., Petrovski, A., & Petrovski, S. (2013). Anomaly monitoring framework based on intelligent data analysis. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 8206 LNCS, pp. 134–141). https://doi.org/10.1007/978-3-642-41278-3_17
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