Extensible and Similarity-based Grouping for Data Integration Eike Schallehn, Kai-Uwe Saatler, and Gunter Saake   View Paper (PDF)   Return to Posters Abstract The general concept of grouping and aggregation appears to be a fitting paradigm for various issues in data integration, but in its common form of equality-based grouping, a number of problems remain unsolved. We propose a generic approach to user-defined grouping as part of a SQL extension, allowing for more complex functions, for instance integration of data mining algorithms. Furthermore, we discuss high-level language primitives for common applications DiSC'03 © 2003 Association for Computing Machinery