Opportunities for Knowledge Discovery in Spatio-Temporal Information Systems

Abstract

Spatial Information Systems and their recent temporal extensions typically store large volumes of geo-referenced information. Having such size, it becomes increasingly difficult to explore their contents with current querying techniques. In this paper, we examine how data mining methods can help users in die analysis of the contents of Spatial and Spatio-Temporal Information Systems. We review existing spatial applications and investigate how they can be extended to deal with time. We also look at new, alternative methods that utilise the inherent structure of spatio-temporal information as well as its rich semantics to derive rules about changes and movement dictated by the querying requirements of the users of Spatio-Temporal Information Systems. The identification of these requirements thus remains important in helping to accommodate data mining techniques within STIS. Our current research concentrates on the development of generic knowledge discovery tools to be used on spatiotemporal data. By incorporating these tools into various types of STIS, we are aiming to provide users with enhanced analytical and data management capabilities. To this effect, we shall define a framework for mining evolution and meta-rules, and test their use in a prototype spatio-temporal knowledge discovery system.

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