Abnormal Time Series Detection in Wireless Sensor Network Based on Hadoop
Zhang Jian-pin · Chuangan jishu xuebao · 2014
In wireless sensor network,the research of abnormal time series detection is of great significance. Due to the poor time efficiency of traditional research under big data,this paper proposes an algorithm about abnormal time series detection based on Hadoop. In this paper,time series are preprocessed firstly and then the DTW algorithm is called during MapReduce operation of Hadoop to realize the parallelization calculation of DTW distance. This measure improves the detection rate greatly. Meanwhile,to solve the bottleneck of computational complexity of classical DTW and the poor precision of the classical constraints,the paper also proposes locally relevant constraints based on salient feature alignments. It constraints the warping path locally to reduce the complexity of time and space further,it also ensures the precision of the algorithm at the same time. The results demonstrate that this algorithm not only decreases the time consumption,but also keeps a high precision.