文献类型：期刊文献

英文题名：Research on the algorithm of hadoop-based spatial-temporal outlier detection

作者：Yao, Lingling[1]; Wang, Zhanquan[1]

机构：[1] Institute of Information Science and Engineering, East China University of Science and Technology, Shanghai, China

年份：2015

起止页码：799

外文期刊名：Proceedings - 5th International Conference on Instrumentation and Measurement, Computer, Communication, and Control, IMCCC 2015

收录：EI(收录号：20161702296383)

语种：英文

外文关键词：Signal detection - Statistics - Big data - Data mining - Fault detection

摘要：A spatial-temporal outlier is an object whose nonspatial attribute value is significantly different from those of other objects in its spatial and temporal neighbors. Identifying or detecting spatial-temporal outliers will help us find some unexpected, interesting and useful knowledge in many application fields, for example: financial fraud detection, fault diagnosis, network intrusion detection and so on. However, the existing spatial-temporal outlier detection algorithms can't efficiently deal with big dataset. In this paper, a Hadoop-based spatial-temporal outlier detection algorithm is proposed. This approach takes the spatial autocorrelation into consideration. Therefore, the weight is introduced in the approach. However, the calculation involved in calculating weight is significantly large. Besides, the big dataset needs to be processed in this approach.Therefore, Hadoop is used to improve it's performance. The Ningbo sea tide dataset is used to validate the effectiveness and scalability of this approach. ? 2015 IEEE.