SKY R-tree: An index structure for distance-based top-k query

Yuya Sasaki, Wang Chien Lee, Takahiro Hara, Shojiro Nishio

Research output: Contribution to journalConference article

4 Citations (Scopus)

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 languageEnglish (US)
Pages (from-to)220-235
Number of pages16
JournalLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume8421 LNCS
Issue numberPART 1
DOIs
StatePublished - Jan 1 2014
Event19th International Conference on Database Systems for Advanced Applications, DASFAA 2014 - Bali, Indonesia
Duration: Apr 21 2014Apr 24 2014

Fingerprint

R-tree
Attribute
Query
Query processing
Program processors
Skyline
Costs
Query Processing
CPU Time
Similarity Measure
Experimentation
Search Space
Proximity
Demonstrate
Object

All Science Journal Classification (ASJC) codes

  • Theoretical Computer Science
  • Computer Science(all)

Cite this

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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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SKY R-tree : An index structure for distance-based top-k query. / Sasaki, Yuya; Lee, Wang Chien; Hara, Takahiro; Nishio, Shojiro.

In: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), Vol. 8421 LNCS, No. PART 1, 01.01.2014, p. 220-235.

Research output: Contribution to journalConference article

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AU - Sasaki, Yuya

AU - Lee, Wang Chien

AU - Hara, Takahiro

AU - Nishio, Shojiro

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AB - 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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