Please use this identifier to cite or link to this item: https://doi.org/10.1109/TKDE.2006.51
Title: Toward efficient multifeature query processing
Authors: Jagadish, H.V.
Ooi, B.C. 
Shen, H.T.
Tan, K.-L. 
Keywords: High-dimensional
Indexing
Multifeature
Query processing
Weighted query
Issue Date: 2006
Source: Jagadish, H.V., Ooi, B.C., Shen, H.T., Tan, K.-L. (2006). Toward efficient multifeature query processing. IEEE Transactions on Knowledge and Data Engineering 18 (3) : 350-361. ScholarBank@NUS Repository. https://doi.org/10.1109/TKDE.2006.51
Abstract: 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 +-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. © 2006 IEEE.
Source Title: IEEE Transactions on Knowledge and Data Engineering
URI: http://scholarbank.nus.edu.sg/handle/10635/38981
ISSN: 10414347
DOI: 10.1109/TKDE.2006.51
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