A Retrieval Technique For High-Dimensional Data And Partially Specified Queries

While the persistent data of many advanced database applications,such as OLAP and scientific studies, arech1EEV erized by very hry dimensionality, typical queries posed onthj# data appeal to a small number of relevant dimensions. Unfortunately, th multi-dimensional access meths1 designed forh1##FD/1T - sional data perform rathm poorly forth1S partially specified queries.Th retrievaltech ique proposed in th1 paper uses a combination of two complementary measures to support e#cient processing of partial queries over hEV/1TSjE sional data. First, an elaborate storage organization, calledth inverted space, allowsth system administrator to controlth size of individual indexes in order to avoidth negative impact of extremely hly data dimensionality on th retrieval performance. Second, a new indexing structure, whe, is designed to supportth inverted-space storage organization, enables e#cient query processing in projected spaces with moderate dimensionality. Th1 indexingmech&/ sm is a general-purpose point access meths th e#ectively attacksth limitations of KDB-trees in spaces with many dimensions, whns preserving th simplicity and relatively good performance of th later structure in low-dimensional spaces.Th analytical and experimental resultsshS thS th new indexingsch me outperforms twooth# variants of KDB-trees investigated in th paper for both fully and partially specified queries