Knowledge Discovery in Spatial Databases
von Hans-Peter Kriegel
(mittwoch, 15.9.99, 9:30 Uhr)
Both, the number and the size of spatial databases, such as geographic or medical databases, are rapidly growing because of the large amount of data obtained from satellite images, X-ray crystallography, computer tomography or other scientific equipment. This growth by far exceeds human capacities to analyze the databases in order to find implicit regularities, rules or clusters hidden in the data. Therefore, automated knowledge discovery becomes more and more important in spatial databases. Knowledge discovery in databases is the non-trivial process of discovering valid, novel, potentially useful, and ultimately understandable patterns from data. Typical tasks for knowledge discovery in spatial databases include clustering, classification, characterization and trend detection. The major differen ce between knowledge discovery in relational databases and in spatial databases is that attributes of the neighbors of some object of interest may have an influence on the object itself. Therefore, spatial knowledge discovery algorithms heavily depend on the efficient processing of neighborhood relations since the neighbors of many objects have to be investigated in a single run of a typical algorithm. Thus, providing general concepts for neighborhood relations as well as an efficient implementation of these concepts will allow a tight integration of spatial knowledge discovery algorithms with a spatial database management system. This will speed up both, the development and the execution of spatial knowledge discovery algorithms. For this purpose, we define a small set of database primitives, and we demonstrate that typical spatial knowledge discovery algorithms are well supported by the proposed database primitives. By implementing the database primitives on top of a commercial spatial database management system, we show the effectiveness and efficiency of our approach, experimentally as well as analytically.
Hans-Peter Kriegel
is a full professor for database systems in the Institute for Computer Science at the University of Munich. His research interests are in spatial database systems, particularly in query processing, performance issues, similarity search, high-dimensional indexing, and in parallel systems. Data Exploration using visualization led him to the area of knowledge discovery and data mining. Kriegel received his M.Sc. and Ph.D. in 1973 and 1976, respectively, from the University of Karlsruhe, Germany. Hans- Peter Kriegel has been chairman and program committee member in many international database conferences. He has published over 150 refereed conference and journal papers.
Homepage: http://www.dbs.informatik.uni-muenchen.de/Mitarbeiter/kriegel.html
22. Juli 1999
