Abstract
With the advent of GIS, multi-media, and warehousing technologies, database systems have started focusing on storage and access of multi-dimensional data such as spatial, OLAP, image, audio, and video attributes. As a step in this direction, Oracle% launched the interMedia product to support spatial and image data, and Materialized Views (MV) to support warehousing applications. Although 2-dimensional spatial data is efficiently indexed using OracleBi Spatial, and highdimensional image data using a combination of bitmap indexing and the Visual Information Retrieval (VIR) product, there is still a need for efficient indexing mechanisms for medium-dimensionality data such as OLAP, and CAD/CAM applications. In this paper, we describe the implementation of a new indextype, called the R-tree, to support medium-dimensionality data (i.e., data whose dimensionality is in the range of 310) using the extensible indexing framework [DDSS95] of OracleSi. This indextype combines some of the best features of existing R-tree variants [Gut84, BKSSSO, WJ96, LLE97, RKV95].
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