Research on the algorithm of Hadoop-based Spatial- Temporal Outlier Detection
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 cant 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 its performance.The Ningbo sea tide dataset is used to validate the effectiveness and scalability of this approach.
spatial outlier detection spatial data mining Hadoop algorithm
Lingling Yao Zhanquan Wang
Institute of information science and engineering East China University of Science and Technology Shanghai, China
国际会议
秦皇岛
英文
799-805
2015-09-18（万方平台首次上网日期，不代表论文的发表时间）