Abstract: A method and apparatus for implementation in a database management system transforms high-dimensional data points to a single-dimensional space so that single-dimensional values can be used as representative index keys for high-dimensional data points and a single-dimensional index structure can be employed to index the transformed values. Upon achieving transformed values, known single-dimensional indexing structures can be employed. To achieve transformation from high-dimensions to a single-dimension, attribute values of a data item, each representing a different dimension, are mapped into a range and an integer value is assigned to each dimension. Either the minimum or maximum dimension value for the multi-dimensional data item is selected, and the minimum or maximum dimensional value is added to the integer value.

Abstract: We disclose a transformation-based method for indexing high-dimensional data to support similarity search. The method, iDistance, partitions the data into clusters either based on some clustering strategies or simple data space partitioning strategies. The data in each cluster can be described based on their similarity with respect to a reference point, and hence they can be transformed into a single dimensional space based on such relative similarity. This allows us to index the data points using a B+-tree structure and perform similarity search using range search strategy. As such, the method is well suited for integration into existing DBMSs. We also study two data partitioning strategies, and several methods on selection of reference points. We conducted extensive experiments to evaluate iDistance, and our results demonstrate its effectiveness.