1. anomaly detection techniques and challenges on big data
Authors : Dr Lalitha T, Dr. K. Kamaraj , Devan M
Pages : 95-99
DOI : http://dx.doi.org/10.21172/1.93.18
Keywords : Big data,Anomaly,Detection,Clustering Abstract :One of the signature traits of big data is that large volumes are created in short periods of time. This data often comes from connected devices, such as mobile phones, vehicle fleets or industrial machinery. The reasons for generating and observing this data are many, yet a common problem is the detection of anomalous behaviour. This may be a machine in a factory that is on the verge of malfunctioning, say due to the imminent breaking of some part, or a member of a vehicle fleet that has experienced unusual or hazardous environmental conditions.Performing predictive modeling, 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.
Citing this Journal Article :Dr Lalitha T, Dr. K. Kamaraj , Devan M, " anomaly detection techniques and challenges on big data", Volume 9 Issue 3 - January 2018, 95-99
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