Sebastian Leuoth

Profit of extending standard relational databases with the Intelligent Cluster Index (ICIx)

In this paper, we present a strategy to reduce the processing time needed for selection operations with many attributes in standard database systems. These problems mostly occur in data mining, data analysis, information retrieval, and applications with high combinatorial complexity. In these systems, standard indexes do not gain a satisfying performance. Currently, this problem is tackled using more computing power or special solutions instead of standard databases. Our approach is to interpret the queries as high-dimensional point or range queries. Thus, we provide a “real” solution to answering complex queries rather than merely postponing the problems using technical methods. We show the benefit of multidimensional data structures. This benefit can be transferred to a lot of applications (e.g. Business Intelligence, Bill Of Materials Explosion, DNA Sequence Search), not only advanced applications of database systems like GIS, CAD, or multimedia. Finally, a very small sample data set is used to show the profit of our approach and we present possible integration points of the ICIx into standard relational database.