A rapid anomaly detection technique for big data curation

Abstract

Anomaly detection (outlier) using simulation helps us analyze the anomaly instances from big data source. As the hasty explosion of today's data stream, outlier detection technique will be an analytical tool to be employed for evaluating massive unstructured datasets. In order to speed-up the processing time to handle enormous datasets, this research will conduct experiments of advanced distant-based outlier detection algorithms to investigate the most effective algorithms using MOA. The algorithms used in this study are Continuous Outlie Detection (COD), Micro-Cluster based COD or MCOD, and STream OutlierR Miner (STORM). The results demonstrate MCOD algorithm can outperform other two algorithms in terms of processing time and accurate anomalies.