The increasing use of temporal and spatial data in present-day relational systems necessitates an efficient support of joins on interval-valued attributes. Standard join algorithms do not support those data types adequately, whereas special approaches for interval joins usually require an augmentation of the internal access methods which is not supported by existing relational systems. To overcome these problems we introduce new join algorithms for interval data. Based on the Relational Interval Tree, these algorithms can easily be implemented on top of any relational database system while providing excellent performance on joining intervals. As experimental results on an Oracle9i server show, the new techniques outperform existing relational methods for joining intervals significantly.

The management of product-related data in a uniform and consistent way is a big challenge for many manufacturing enterprises, especially the large ones, like DaimlerChrysler. So-called product data management (PDM) systems are a promising way to achieve this goal. For various reasons, PDM systems often sit on top of a relational DBMS, using it (more or less) as a simple record manager. User interactions with the PDM systems are translated into a series of SQL queries. This does not cause too much harm when the DBMS and PDM system are located in the same local area network, with high bandwidth and short latency times. The picture may change dramatically, however, if the users are working in geographically distributed environments. Response times may rise by orders of magnitude, e.g. from 1-2 minutes in the local context to 30 minutes and even more in the "inter-continental" context. This paper shows how a more sophisticated utilization of the (advanced) SQL features coming along with SQL:1999 can help to cut down response times significantly.