Performance Optimization of Database Operations in Spatial Database Systems

Knowledge discovery in databases (KDD) is an important task in spatial databases since both, the number and the size of such databases are rapidly growing. The automated discovery of knowledge in databases is becoming increasingly important as the world’s wealth of data continues to grow exponentially. The main contribution of this paper is to introduce a set of basic operations, which should be supported by a spatial database system (SDBS) to express algorithms for KDD in SDBS. The definition of such a set of basic operations and their efficient support by an SDBS will speed up the development of new spatial KDD algorithms and their performance. For this purpose, we introduce the concept of neighborhood graphs and paths and a small set of operations for their manipulation. These operations are sufficient for KDD algorithms considering spatial neighborhood relations by presenting the implementation of typical spatial KDD algorithms based on the proposed operations. A wide variety of algorithms have been proposed for KDD. This involves evaluation of algorithms for optimizing the performance of the KDD operations. These algorithms are classified and identified certain generic tasks like cluster, classification, dependency analysis and deviation detection. While a lot of algorithms have been developed for KDD in relational databases, the area of KDD in spatial databases has only recently emerged. Furthermore, the efficient support of operations on large neighborhood graphs and on large sets of neighborhood paths by the SDBS is discussed. Neighborhood indices are introduced to materialize selected neighborhood graphs in order to speed up the processing of database operations. For that, firstly, the algorithms for optimizing the performance of the KDD operations using the available indices are to be evaluated. Secondly, the materialization of neighborhood paths has to be investigated. This seems to be feasible if appropriate filters are used to create reasonably small sets of paths. A materialization of relevant paths may further speed-up the overall performance of KDD tasks because different KDD algorithms may use the same set of paths and each algorithm may this scan set of paths many times. Finally, applications in such domains as geography, biology or CAD will be investigated to insure the practical impact of our approach.