文章基本信息

标题：Challenging Issues of Spatio-Temporal Data Mining
本地全文：下载
作者：A.N.M. Bazlur Rashid ; Md. Anwar Hossain
期刊名称：Computer Engineering and Intelligent Systems
印刷版ISSN：2222-1727
电子版ISSN：2222-2863
出版年度：2012
卷号：3
期号：4
页码：55-63
语种：English
出版社：International Institute for Science, Technology Education
摘要：The spatio-temporal database (STDB) has received considerable attention during the past few years, due to the emergence of numerous applications ( e.g. , flight control systems, weather forecast, mobile computing, etc. ) that demand efficient management of moving objects. These applications record objects' geographical locations (sometimes also shapes) at various timestamps and support queries that explore their historical and future (predictive) behaviors. The STDB significantly extends the traditional spatial database , which deals with only stationary data and hence is inapplicable to moving objects, whose dynamic behavior requires re-investigation of numerous topics including data modeling, indexes, and the related query algorithms. In many application areas, huge amounts of data are generated, explicitly or implicitly containing spatial or spatiotemporal information. However, the ability to analyze these data remains inadequate, and the need for adapted data mining tools becomes a major challenge. In this paper, we have presented the challenging issues of spatio-temporal data mining.
关键词：database; data mining; spatial; temporal; spatio-temporal