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A Cyclic Sliced Partitioning Method for Packing High-dimensional Data  

김태완 (부산대학교 컴퓨터및정보통신연구소)
이기준 (부산대학교 전자계산학과)
Publication Information
Journal of KIISE:Databases / v.31, no.2, 2004 , pp. 122-131 More about this Journal
Abstract
Traditional works on indexing have been suggested for low dimensional data under dynamic environments. But recent database applications require efficient processing of huge sire of high dimensional data under static environments. Thus many indexing strategies suggested especially in partitioning ones do not adapt to these new environments. In our study, we point out these facts and propose a new partitioning strategy, which complies with new applications' requirements and is derived from analysis. As a preliminary step to propose our method, we apply a packing technique on the one hand and exploit observations on the Minkowski-sum cost model on the other, under uniform data distribution. Observations predict that unbalanced partitioning strategy may be more query-efficient than balanced partitioning strategy for high dimensional data. Thus we propose our method, called CSP (Cyclic Spliced Partitioning method). Analysis on this method explicitly suggests metrics on how to partition high dimensional data. By the cost model, simulations, and experiments, we show excellent performance of our method over balanced strategy. By experimental studies on other indices and packing methods, we also show the superiority of our method.
Keywords
High dimensional data; Access Method; Packing; Cost Model;
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Times Cited By KSCI : 1  (Citation Analysis)
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