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A novel Outlier Detection Algorithm for Distributed Databases

文献类型： 外文期刊

作者： Zhou, Jiaogen 1 ; Zhao, Chunjiang 1 ; Wan, You 3 ; Huang, Wenjiang 1 ; Yang, Baozhu 1 ; Ge, Jixin 4 ;

作者机构： 1.NERCITA, Beijing Acad Agr & Forestry Sci, Beijing 100097, Peoples R China

2.IRSA, Chinese Acad Sci, Beijing 100101, Peoples R China

3.Wuhan Univ, Int Sch Software, Beijing 430079, Peoples R China

4.Informat Ctr Beijing Municipal Rural Affair Comm, Beijing 100085, Peoples R China

期刊名称：FIFTH INTERNATIONAL CONFERENCE ON FUZZY SYSTEMS AND KNOWLEDGE DISCOVERY, VOL 5, PROCEEDINGS

ISSN：

年卷期： 2008 年

页码：

收录情况： SCI

摘要： Traditional outlier detection algorithms are designed to apply to centralized databases, not distributed databases. We proposed a novel outlier detection algorithm for distributed databases. Given data assigned to different network nodes of a network platform, where each node has its own memory and hard disc, and the communication between nodes driven by message, the populated data would be non-overlapping. The working way of the network system is a manager-worker mode, that is, that a node as manager is responsible for assigning tasks to worker and querying the results from worker nodes. The algorithm first detected local outliers based on distance on all nodes, and then identified local outliers collected in the central node where a globally screening operation on all local outliers was implemented to achieve really global outliers. To scale the algorithm to massive data and reduce its computing complexity, a data filtering technology was further presented. Experimental results demonstrated that the algorithm effectively and efficiently handled on real and artificial data.

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