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The spatio-temporal data mining 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 spatio-temporal data mining significantly extends the traditional spatial data mining 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 spatiatemporal information. However, the ability to analyze these data remains inadequate & the need for adapted data mining tools becomes a major challenge. The detail analysis of spatio-temporal data mining and related topics should be useful to the professionals and researchers implementing & improving this type of mining.
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