Spatiotemporal Data Mining: Issues, Tasks And Applications

Abstract

Spatiotemporal data usually contain the states of an object, an event or a position in space over a period of time. Vast amount of spatiotemporal data can be found in several application fields such as traffic management, environment monitoring, and weather forecast. These datasets might be collected at different locations at various points of time in different formats. It poses many challenges in representing, processing, analysis and mining of such datasets due to complex structure of spatiotemporal objects and the relationships among them in both spatial and temporal dimensions. In this paper, the issues and challenges related to spatiotemporal data representation, analysis, mining and visualization of knowledge are presented. Various kinds of data mining tasks such as association rules, classification clustering for discovering knowledge from spatiotemporal datasets are examined and reviewed. System functional requirements for such kind of knowledge discovery and database structure are discussed. Finally applications of spatiotemporal data mining are presented.