Knowledge Discovery in Spatial Databases

Knowledge discovery in databases (KDD) has been defined as the process of discovering valid, novel, and potentially useful patterns from data [9]. Spatial Database Systems (SDBS) (see [10] for an overview) are database systems for the management of spatial data. To find implicit regularities, rules or patterns hidden in large spatial databases, e.g. for geo-marketing, traffic control or environmental studies, spatial data mining algorithms are very important (see [12] for an overview). Most existing data mining algorithms run on separate and specially prepared files, but integrating them with a database management system (DBMS) has the following advantages. Redundant storage and potential inconsistencies can be avoided. Furthermore, commercial database systems offer various index structures to support different types of database queries. This functionality can be used without extra implementation effort to speed-up the execution of data mining algorithms. Similar to the relational standard query language SQL, the use of standard primitives will speed-up the development of new data mining algorithms and will also make them more portable. In this paper, we introduce a set of database primitives for mining in spatial databases. [1] follows a similar approach for mining in relational databases. Our database primitives (section 2) are based on the concept of neighborhood relations. The proposed primitives are sufficient to express most of the algorithms for spatial data mining from the literature (section 3). We present techniques for efficiently supporting these primitives by a DBMS (section 4). Section 5 summarizes the contributions and discusses several issues for future research.

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