Paper Info
Title
Contextual Anomaly Detection in Big Sensor Data
Abstract
Performing predictive modelling, such as anomaly detection, in Big Data is a difficult task. This problem is compounded as more and more sources of Big Data are generated from environmental sensors, logging applications, and the Internet of Things. Further, most current techniques for anomaly detection only consider the content of the data source, i.e. the data itself, without concern for the context of the data. As data becomes more complex it is increasingly important to bias anomaly detection techniques for the context, whether it is spatial, temporal, or semantic. The work proposed in this paper outlines a contextual anomaly detection technique for use in streaming sensor networks. The technique uses a well-defined content anomaly detection algorithm for real-time point anomaly detection. Additionally, we present a post-processing context-aware anomaly detection algorithm based on sensor profiles, which are groups of contextually similar sensors generated by a multivariate clustering algorithm. Our proposed research has been implemented and evaluated with real-world data provided by Powersmiths, located in Brampton, Ontario, Canada.
Year
DOI
Venue
2014
10.1109/BigData.Congress.2014.19
BigData Congress
Keywords
Field
DocType
big data analytics, contextual anomaly detection, predictive modelling, multivariate clustering,big data analytics,predictive modelling
Data source,Anomaly detection,Data mining,Computer science,Internet of Things,Predictive modelling,Cluster analysis,Big data,Detector,Wireless sensor network
Conference
ISSN
Citations 
PageRank 
2379-7703
10
0.55
References 
Authors
9
2
Name
Order
Citations
PageRank
Michael A. Hayes1231.13
Miriam A. M. Capretz2100.55