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.
Original language | English (US) |
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Pages (from-to) | 220-235 |
Number of pages | 16 |
Journal | Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) |
Volume | 8421 LNCS |
Issue number | PART 1 |
DOIs | |
State | Published - 2014 |
Event | 19th International Conference on Database Systems for Advanced Applications, DASFAA 2014 - Bali, Indonesia Duration: Apr 21 2014 → Apr 24 2014 |
All Science Journal Classification (ASJC) codes
- Theoretical Computer Science
- Computer Science(all)