The Community for Technology Leaders
Green Image
Issue No. 03 - March (2006 vol. 18)
ISSN: 1041-4347
pp: 350-362
Kian-Lee Tan , IEEE Computer Society
In many advanced applications, data are described by multiple high-dimensional features. Moreover, different queries may weight these features differently; some may not even specify all the features. In this paper, we propose our solution to support efficient query processing in these applications. We devise a novel representation that compactly captures f features into two components: The first component is a 2D vector that reflects a distance range (minimum and maximum values) of the f features with respect to a reference point (the center of the space) in a metric space and the second component is a bit signature, with two bits per dimension, obtained by analyzing each feature's descending energy histogram. This representation enables two levels of filtering: The first component prunes away points that do not share similar distance ranges, while the bit signature filters away points based on the dimensions of the relevant features. Moreover, the representation facilitates the use of a single index structure to further speed up processing. We employ the classical B^+{\hbox{-}}\rm tree for this purpose. We also propose a KNN search algorithm that exploits the access orders of critical dimensions of highly selective features and partial distances to prune the search space more effectively. Our extensive experiments on both real-life and synthetic data sets show that the proposed solution offers significant performance advantages over sequential scan and retrieval methods using single and multiple VA-files.
Index Terms- Multifeature, indexing, query processing, high-dimensional, weighted query.

B. C. Ooi, K. Tan, H. Jagadish and H. T. Shen, "Toward Efficient Multifeature Query Processing," in IEEE Transactions on Knowledge & Data Engineering, vol. 18, no. , pp. 350-362, 2006.
375 ms
(Ver 3.3 (11022016))