Alexander Adam

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.

External Database Extension Framework

Database systems nowadays show an incredible amount of extensibility interfaces, ranging from simple user defined functions to extensible indexing frameworks as seen in e. g. DB2 and Oracle. Approaching new projects using these interfaces definitely is a valuable option. For legacy systems, already set up and running in production environments, these options are often not available since most of them impose a change in the applications. In this work we present a database extension framework, that enables the user to define functionality which does not reside inside the database. We show different ways to integrate it into existing application landscapes without further modifications.