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Efficient continuous top-k spatial keyword queries on road networks

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Abstract

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.

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Notes

  1. In this paper, we use an undirected graph G(N,E) and transmit the direction of q explicitly. We cannot model the road network as a directed graph and get the direction of q from the directed graph. The reason is that if one road is bidirectional, the direction cannot be obtained.

  2. Note that for the CkSK queries, the generators are the relevant objects in O r that match at least one of the query keywords in their descriptions.

References

  1. Anh VN, de Kretser O, Moffat A (2001) Vector-space ranking with effective early termination. In: SIGIR. pp 35–42

  2. Brinkhoff T (2002) A framework for generating network-based moving objects. GeoInformatica 6(2):153–180

    Article  Google Scholar 

  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):1182–1199

    Article  Google Scholar 

  4. Chen L, Cong G, Jensen CS, Wu D (2013) Spatial keyword query processing: an experimental evaluation. In: VLDB. pp 217–228

  5. Chen Z, Shen HT, Zhou X, Yu JX (2009) Monitoring path nearest neighbor in road networks. In: SIGMOD conference. pp 591–602

  6. Cho HJ, Chung CW (2005) An efficient and scalable approach to cnn queries in a road network. In: VLDB. pp 865–876

  7. Cong G, Jensen CS, Wu D (2009) Efficient retrieval of the top-k most relevant spatial web objects. PVLDB 2(1):337–348

    Google Scholar 

  8. Felipe ID, Hristidis V, Rishe N (2008) Keyword search on spatial databases. In: ICDE. pp 656–665

  9. Hu H, Xu J, Lee DL (2005) A generic framework for monitoring continuous spatial queries over moving objects. In: SIGMOD conference. pp 479–490

  10. Huang W, Li G, Tan KL, Feng J (2012) Efficient safe-region construction for moving top-k spatial keyword queries. In: CIKM. pp 932–941

  11. Kolahdouzan MR, Shahabi C (2004) Voronoi-based k nearest neighbor search for spatial network databases. In: VLDB. pp 840–851

  12. Kolahdouzan MR, Shahabi C (2005) Alternative solutions for continuous k nearest neighbor queries in spatial network databases. GeoInformatica 9(4):321–341

    Article  Google Scholar 

  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):1201–1215

    Article  Google Scholar 

  14. Nutanong S, Zhang R, Tanin E, Kulik L (2008) The v∗-diagram: a query-dependent approach to moving knn queries. PVLDB 1(1):1095–1106

    Google Scholar 

  15. Okabe A, Boots B, Sugihara K, Chiu SN (2000) Spatial tessellations: concepts and applications of Voronoi diagrams, 2nd edn. Wiley, Chichester

    Book  Google Scholar 

  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):965–994

    Article  Google Scholar 

  17. Rocha-Junior JB, Gkorgkas O, Jonassen S, Nørvåg K (2011) Efficient processing of top-k spatial keyword queries. In: SSTD. pp 205–222

  18. Rocha-Junior JB, Nørvåg K (2012) Top-k spatial keyword queries on road networks. In: EDBT. pp 168–179

  19. Salton G, Buckley C (1988) Term-weighting approaches in automatic text retrieval. Inf Process Manage 24(5):513–523

    Article  Google Scholar 

  20. Wu D, Yiu ML, Cong G, Jensen CS (2012) Joint top-k spatial keyword query processing. IEEE Trans Knowl Data Eng 24(10):1889–1903

    Article  Google Scholar 

  21. Wu D, Yiu ML, Jensen CS, Cong G (2011) Efficient continuously moving top-k spatial keyword query processing. In: ICDE. pp 541–552

  22. Wu W, Guo W, Tan KL (2007) Distributed processing of moving k-nearest-neighbor query on moving objects. In: ICDE. pp 1116–1125

  23. Zhang C, Zhang Y, Zhang W, Lin X (2013) Inverted linear quadtree: efficient top k spatial keyword search. In: ICDE

  24. Zhang D, Chee YM, Mondal A, Tung AKH, Kitsuregawa M (2009) Keyword search in spatial databases: towards searching by document. In: ICDE. pp 688–699

  25. Zhang D, Tan KL, Tung AKH (2013) Scalable top-k spatial keyword search. In: EDBT. pp 359–370

  26. Zhou Y, Xie X, Wang C, Gong Y, Ma WY (2005) Hybrid index structures for location-based web search. In: CIKM. pp 155–162

  27. Zobel J, Moffat A (2006) Inverted files for text search engines. ACM Comput Surv 38(2)

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Acknowledgments

This work is funded by the NExT Search Centre (grant R-252-300-001-490), which is supported by the Singapore National Research Foundation under its International Research Centre @ Singapore Funding Initiative and administered by the IDM Program Office.

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Authors and Affiliations

  1. School of Computing, National University of Singapore, 13 Computing Drive, Singapore, 117417, Singapore

    Long Guo, Jie Shao, Htoo Htet Aung & Kian-Lee Tan

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  1. Long Guo
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  2. Jie Shao
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Correspondence to Long Guo.

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Guo, L., Shao, J., Aung, H.H. et al. Efficient continuous top-k spatial keyword queries on road networks. Geoinformatica 19, 29–60 (2015). https://doi.org/10.1007/s10707-014-0204-8

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  • DOI: https://doi.org/10.1007/s10707-014-0204-8

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