Abstract
Searches for objects associated with location information and non-spatial attributes have increased significantly over the years. To address this need, a top-k query may be issued by taking into account both the location information and non-spatial attributes. This paper focuses on a distance-based top-k query which retrieves the best objects based on distance from candidate objects to a query point as well as other non-spatial attributes. In this paper, we propose a new index structure and query processing algorithms for distance-based top-k queries. This new index, called SKY R-tree, drives on the strengths of R-tree and Skyline algorithm to efficiently prune the search space by exploring both the spatial proximity and non-spatial attributes. Moreover, we propose a variant of SKY R-tree, called S2KY R-tree which incorporates a similarity measure of non-spatial attributes. We demonstrate, through extensive experimentation, that our proposals perform very well in terms of I/O costs and CPU time.
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Sasaki, Y., Lee, WC., Hara, T., Nishio, S. (2014). SKY R-tree: An Index Structure for Distance-Based Top-k Query. In: Bhowmick, S.S., Dyreson, C.E., Jensen, C.S., Lee, M.L., Muliantara, A., Thalheim, B. (eds) Database Systems for Advanced Applications. DASFAA 2014. Lecture Notes in Computer Science, vol 8421. Springer, Cham. https://doi.org/10.1007/978-3-319-05810-8_15
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DOI: https://doi.org/10.1007/978-3-319-05810-8_15
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