Nearest window cluster queries

Chen Che Huang, Jiun Long Huang, Tsung Ching Liang, Jun Zhe Wang, Wen Yuah Shih, Wang-chien Lee

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Citation (Scopus)

Abstract

In this paper, we study a novel type of spatial queries, namely Nearest Window Cluster (NWC) queries. For a given query location q, NWC (q,l,w,n) retrieves n objects within a window of length l and width w, where the distance between the query location q to these n objects is the shortest. To facilitate efficient NWC query processing, we identify several properties and accordingly develop an NWC algorithm. Moreover, we propose several optimization techniques to further reduce the search cost. To validate our ideas, we conduct a comprehensive performance evaluation using both real and synthetic datasets. Experimental results show that the proposed NWC algorithm, along with the optimization techniques, is very efficient under various datasets and parameter settings. Keywords: Nearest window cluster query, spatial query processing, location-based service, spatial database.

Original languageEnglish (US)
Title of host publicationAdvances in Database Technology - EDBT 2016
Subtitle of host publication19th International Conference on Extending Database Technology, Proceedings
EditorsIoana Manolescu, Evaggelia Pitoura, Amelie Marian, Sofian Maabout, Letizia Tanca, Georgia Koutrika, Kostas Stefanidis
PublisherOpenProceedings.org
Pages341-352
Number of pages12
ISBN (Electronic)9783893180707
DOIs
StatePublished - Jan 1 2016
Event19th International Conference on Extending Database Technology, EDBT 2016 - Bordeaux, France
Duration: Mar 15 2016Mar 18 2016

Publication series

NameAdvances in Database Technology - EDBT
Volume2016-March
ISSN (Electronic)2367-2005

Other

Other19th International Conference on Extending Database Technology, EDBT 2016
CountryFrance
CityBordeaux
Period3/15/163/18/16

Fingerprint

Query processing
Location based services
Costs

All Science Journal Classification (ASJC) codes

  • Information Systems
  • Software
  • Computer Science Applications

Cite this

Huang, C. C., Huang, J. L., Liang, T. C., Wang, J. Z., Shih, W. Y., & Lee, W. (2016). Nearest window cluster queries. In I. Manolescu, E. Pitoura, A. Marian, S. Maabout, L. Tanca, G. Koutrika, & K. Stefanidis (Eds.), Advances in Database Technology - EDBT 2016: 19th International Conference on Extending Database Technology, Proceedings (pp. 341-352). (Advances in Database Technology - EDBT; Vol. 2016-March). OpenProceedings.org. https://doi.org/10.5441/002/edbt.2016.32
Huang, Chen Che ; Huang, Jiun Long ; Liang, Tsung Ching ; Wang, Jun Zhe ; Shih, Wen Yuah ; Lee, Wang-chien. / Nearest window cluster queries. Advances in Database Technology - EDBT 2016: 19th International Conference on Extending Database Technology, Proceedings. editor / Ioana Manolescu ; Evaggelia Pitoura ; Amelie Marian ; Sofian Maabout ; Letizia Tanca ; Georgia Koutrika ; Kostas Stefanidis. OpenProceedings.org, 2016. pp. 341-352 (Advances in Database Technology - EDBT).
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abstract = "In this paper, we study a novel type of spatial queries, namely Nearest Window Cluster (NWC) queries. For a given query location q, NWC (q,l,w,n) retrieves n objects within a window of length l and width w, where the distance between the query location q to these n objects is the shortest. To facilitate efficient NWC query processing, we identify several properties and accordingly develop an NWC algorithm. Moreover, we propose several optimization techniques to further reduce the search cost. To validate our ideas, we conduct a comprehensive performance evaluation using both real and synthetic datasets. Experimental results show that the proposed NWC algorithm, along with the optimization techniques, is very efficient under various datasets and parameter settings. Keywords: Nearest window cluster query, spatial query processing, location-based service, spatial database.",
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Huang, CC, Huang, JL, Liang, TC, Wang, JZ, Shih, WY & Lee, W 2016, Nearest window cluster queries. in I Manolescu, E Pitoura, A Marian, S Maabout, L Tanca, G Koutrika & K Stefanidis (eds), Advances in Database Technology - EDBT 2016: 19th International Conference on Extending Database Technology, Proceedings. Advances in Database Technology - EDBT, vol. 2016-March, OpenProceedings.org, pp. 341-352, 19th International Conference on Extending Database Technology, EDBT 2016, Bordeaux, France, 3/15/16. https://doi.org/10.5441/002/edbt.2016.32

Nearest window cluster queries. / Huang, Chen Che; Huang, Jiun Long; Liang, Tsung Ching; Wang, Jun Zhe; Shih, Wen Yuah; Lee, Wang-chien.

Advances in Database Technology - EDBT 2016: 19th International Conference on Extending Database Technology, Proceedings. ed. / Ioana Manolescu; Evaggelia Pitoura; Amelie Marian; Sofian Maabout; Letizia Tanca; Georgia Koutrika; Kostas Stefanidis. OpenProceedings.org, 2016. p. 341-352 (Advances in Database Technology - EDBT; Vol. 2016-March).

Research output: Chapter in Book/Report/Conference proceedingConference contribution

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N2 - In this paper, we study a novel type of spatial queries, namely Nearest Window Cluster (NWC) queries. For a given query location q, NWC (q,l,w,n) retrieves n objects within a window of length l and width w, where the distance between the query location q to these n objects is the shortest. To facilitate efficient NWC query processing, we identify several properties and accordingly develop an NWC algorithm. Moreover, we propose several optimization techniques to further reduce the search cost. To validate our ideas, we conduct a comprehensive performance evaluation using both real and synthetic datasets. Experimental results show that the proposed NWC algorithm, along with the optimization techniques, is very efficient under various datasets and parameter settings. Keywords: Nearest window cluster query, spatial query processing, location-based service, spatial database.

AB - In this paper, we study a novel type of spatial queries, namely Nearest Window Cluster (NWC) queries. For a given query location q, NWC (q,l,w,n) retrieves n objects within a window of length l and width w, where the distance between the query location q to these n objects is the shortest. To facilitate efficient NWC query processing, we identify several properties and accordingly develop an NWC algorithm. Moreover, we propose several optimization techniques to further reduce the search cost. To validate our ideas, we conduct a comprehensive performance evaluation using both real and synthetic datasets. Experimental results show that the proposed NWC algorithm, along with the optimization techniques, is very efficient under various datasets and parameter settings. Keywords: Nearest window cluster query, spatial query processing, location-based service, spatial database.

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Huang CC, Huang JL, Liang TC, Wang JZ, Shih WY, Lee W. Nearest window cluster queries. In Manolescu I, Pitoura E, Marian A, Maabout S, Tanca L, Koutrika G, Stefanidis K, editors, Advances in Database Technology - EDBT 2016: 19th International Conference on Extending Database Technology, Proceedings. OpenProceedings.org. 2016. p. 341-352. (Advances in Database Technology - EDBT). https://doi.org/10.5441/002/edbt.2016.32