, Volume 19, Issue 1, pp 29–60 | Cite as

Efficient continuous top-k spatial keyword queries on road networks



With the development of GPS-enabled mobile devices, more and more pieces of information on the web are geotagged. Spatial keyword queries, which consider both spatial locations and textual descriptions to find objects of interest, adapt well to this trend. Therefore, a considerable number of studies have focused on the interesting problem of efficiently processing spatial keyword queries. However, most of them assume Euclidean space or examine a single snapshot query only. This paper investigates a novel problem, namely, continuous top-k spatial keyword queries on road networks, for the first time. We propose two methods that can monitor such moving queries in an incremental manner and reduce repetitive traversing of network edges for better performance. Experimental evaluation using large real datasets demonstrates that the proposed methods both outperform baseline methods significantly. Discussion about the parameters affecting the efficiency of the two methods is also presented to reveal their relative advantages.


Top-k spatial keyword queries Continuous queries Road networks 


  1. 1.
    Anh VN, de Kretser O, Moffat A (2001) Vector-space ranking with effective early termination. In: SIGIR. pp 35–42Google Scholar
  2. 2.
    Brinkhoff T (2002) A framework for generating network-based moving objects. GeoInformatica 6(2):153–180CrossRefGoogle Scholar
  3. 3.
    Cheema MA, Brankovic L, Lin X, Zhang W, Wang W (2011) Continuous monitoring of distance-based range queries. IEEE Trans Knowl Data Eng 23(8):1182–1199CrossRefGoogle Scholar
  4. 4.
    Chen L, Cong G, Jensen CS, Wu D (2013) Spatial keyword query processing: an experimental evaluation. In: VLDB. pp 217–228Google Scholar
  5. 5.
    Chen Z, Shen HT, Zhou X, Yu JX (2009) Monitoring path nearest neighbor in road networks. In: SIGMOD conference. pp 591–602Google Scholar
  6. 6.
    Cho HJ, Chung CW (2005) An efficient and scalable approach to cnn queries in a road network. In: VLDB. pp 865–876Google Scholar
  7. 7.
    Cong G, Jensen CS, Wu D (2009) Efficient retrieval of the top-k most relevant spatial web objects. PVLDB 2(1):337–348Google Scholar
  8. 8.
    Felipe ID, Hristidis V, Rishe N (2008) Keyword search on spatial databases. In: ICDE. pp 656–665Google Scholar
  9. 9.
    Hu H, Xu J, Lee DL (2005) A generic framework for monitoring continuous spatial queries over moving objects. In: SIGMOD conference. pp 479–490Google Scholar
  10. 10.
    Huang W, Li G, Tan KL, Feng J (2012) Efficient safe-region construction for moving top-k spatial keyword queries. In: CIKM. pp 932–941Google Scholar
  11. 11.
    Kolahdouzan MR, Shahabi C (2004) Voronoi-based k nearest neighbor search for spatial network databases. In: VLDB. pp 840–851Google Scholar
  12. 12.
    Kolahdouzan MR, Shahabi C (2005) Alternative solutions for continuous k nearest neighbor queries in spatial network databases. GeoInformatica 9(4):321–341CrossRefGoogle Scholar
  13. 13.
    Nutanong S, Tanin E, Shao J, Zhang R, Ramamohanarao K (2012) Continuous detour queries in spatial networks. IEEE Trans Knowl Data Eng 24(7):1201–1215CrossRefGoogle Scholar
  14. 14.
    Nutanong S, Zhang R, Tanin E, Kulik L (2008) The v-diagram: a query-dependent approach to moving knn queries. PVLDB 1(1):1095–1106Google Scholar
  15. 15.
    Okabe A, Boots B, Sugihara K, Chiu SN (2000) Spatial tessellations: concepts and applications of Voronoi diagrams, 2nd edn. Wiley, ChichesterCrossRefGoogle Scholar
  16. 16.
    Okabe A, Satoh T, Furuta T, Suzuki A, Okano K (2008) Generalized network voronoi diagrams: concepts, computational methods, and applications. Int J Geogr Inf Sci 22(9):965–994CrossRefGoogle Scholar
  17. 17.
    Rocha-Junior JB, Gkorgkas O, Jonassen S, Nørvåg K (2011) Efficient processing of top-k spatial keyword queries. In: SSTD. pp 205–222Google Scholar
  18. 18.
    Rocha-Junior JB, Nørvåg K (2012) Top-k spatial keyword queries on road networks. In: EDBT. pp 168–179Google Scholar
  19. 19.
    Salton G, Buckley C (1988) Term-weighting approaches in automatic text retrieval. Inf Process Manage 24(5):513–523CrossRefGoogle Scholar
  20. 20.
    Wu D, Yiu ML, Cong G, Jensen CS (2012) Joint top-k spatial keyword query processing. IEEE Trans Knowl Data Eng 24(10):1889–1903CrossRefGoogle Scholar
  21. 21.
    Wu D, Yiu ML, Jensen CS, Cong G (2011) Efficient continuously moving top-k spatial keyword query processing. In: ICDE. pp 541–552Google Scholar
  22. 22.
    Wu W, Guo W, Tan KL (2007) Distributed processing of moving k-nearest-neighbor query on moving objects. In: ICDE. pp 1116–1125Google Scholar
  23. 23.
    Zhang C, Zhang Y, Zhang W, Lin X (2013) Inverted linear quadtree: efficient top k spatial keyword search. In: ICDEGoogle Scholar
  24. 24.
    Zhang D, Chee YM, Mondal A, Tung AKH, Kitsuregawa M (2009) Keyword search in spatial databases: towards searching by document. In: ICDE. pp 688–699Google Scholar
  25. 25.
    Zhang D, Tan KL, Tung AKH (2013) Scalable top-k spatial keyword search. In: EDBT. pp 359–370Google Scholar
  26. 26.
    Zhou Y, Xie X, Wang C, Gong Y, Ma WY (2005) Hybrid index structures for location-based web search. In: CIKM. pp 155–162Google Scholar
  27. 27.
    Zobel J, Moffat A (2006) Inverted files for text search engines. ACM Comput Surv 38(2)Google Scholar

Copyright information

© Springer Science+Business Media New York 2014

Authors and Affiliations

  • Long Guo
    • 1
  • Jie Shao
    • 1
  • Htoo Htet Aung
    • 1
  • Kian-Lee Tan
    • 1
  1. 1.School of ComputingNational University of SingaporeSingaporeSingapore

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