Towards QPD: Queries with Partial Dimensions

Yaokai Feng, Zhibin Wang, Akifumi Makinouchi, Ryu Hiroshi

研究成果: Contribution to journalArticle


Multidimensional indices are very helpful to improve query performance on multidimensional data. The existing multidimensional indices are directed to "queries with all dimensions" (called QAD in this study). That is, the dimensions used in each query are all the dimensions in the whole space. However, in many applications, the queries may be only with some (partial) dimensions (not all) of the whole space, which is called QPD (Queries with Partial Dimensions). If the existing multidimensional indices are used in range QPDs, the dimensions unused in the query are thought as spanning the whole data ranges, which often lead to not-good search performance. In these cases, certainly, we also can construct many indices with all the necessary combination of dimensions. However, this is very space/time-consuming since many indices have to be constructed and some dimensions may be used many times in different indices, which is not always feasible. In this study, we propose a novel solution to RQPD problem. With our solution, only one index is necessary to such applications. The performance of our solution is discussed in detail and is examined by experiments.
ジャーナルIEICE technical report
出版ステータス出版済み - 7 6 2004


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