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Multiple Query Point Based Collective Spatial Keyword Querying

  • Yun LiEmail author
  • Ziheng Wang
  • Jing Chen
  • Fei Wang
  • Jiajie Xu
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11888)

Abstract

Spatial keyword search is a useful technique to enable users find the spatial web object they prefer. Since they objects spatially close to the query point may not fulfill all query objectives, collective spatial keyword query aims to retrieve a group of objects that can cover all required query keywords while properly located in spatial. However in some cases, the querying may be subject to several people in different locations together, and the returned group of objects should not only cover all of their objectives, but also optimal regarding to all of the related people. To this end, this paper studies the problem of multiple query point based collective spatial keyword querying (MCSKQ). Two novel algorithms, HCQ and BCQ, are proposed to support efficient collective query processing w.r.t. multiple query points. The experimental results and related analysis show that MCSKQ has good efficiency and accuracy performance.

Keywords

Spatial textual object CSKQ Multiple queries Location-based services Collaborative search 

References

  1. 1.
    Cao, X., et al.: Spatial keyword querying. In: Atzeni, P., Cheung, D., Ram, S. (eds.) ER 2012. LNCS, vol. 7532, pp. 16–29. Springer, Heidelberg (2012).  https://doi.org/10.1007/978-3-642-34002-4_2CrossRefGoogle Scholar
  2. 2.
    Cao, X., Chen, L., Gao, C., Xiao, X.: Keyword-aware optimal route search. VLDB 5(11), 1136–1147 (2012)Google Scholar
  3. 3.
    Cao, X., Cong, G., Jensen, C.S., Ooi, B.C.: Collective spatial keyword querying. In: SIGMOD 2011, pp. 373–384 (2011)Google Scholar
  4. 4.
    Chen, L., Lin, X., Hu, H., Jensen, C.S., Xu, J.: Answering why-not questions on spatial keyword top-k queries. In: ICDE 2015, pp. 279–290 (2015)Google Scholar
  5. 5.
    Chen, L., Cong, G., Jensen, C.S., Wu, D.: Spatial keyword query processing: an experimental evaluation. VLDB 6(3), 217–228 (2013)Google Scholar
  6. 6.
    Cong, G., Jensen, C.S., Wu, D.: Efficient retrieval of the top-k most relevant spatial web objects. VLDB 2(1), 337–348 (2009)Google Scholar
  7. 7.
    Guttman, A.: R-trees: a dynamic index structure for spatial searching. In: SIGMOD 1984, pp. 47–57 (1984)CrossRefGoogle Scholar
  8. 8.
    Rocha-Junior, J.: Top-k spatial keyword queries on road networks. In: ICEDT 2012, pp. 168–179 (2012)Google Scholar
  9. 9.
    Li, G., Feng, J., Xu, J.: DESKS: direction-aware spatial keyword search. In: ICEDT 2012, pp. 474–485 (2012)Google Scholar
  10. 10.
    Long, C., Wong, C.W., Wang, K., Fu, W.C.: Collective spatial keyword queries: a distance owner-driven approach. In: SIGMOD 2013, pp. 689–700 (2013)Google Scholar
  11. 11.
    Lu, J., Lu, Y., Cong, G.: Reverse spatial and textual k nearest neighbor search. In: SIGMOD 2011, pp. 349–360 (2011)Google Scholar
  12. 12.
    Wu, D., Man, L.Y., Jensen, C.S., Cong, G.: Efficient continuously moving top-k spatial keyword query processing. In: ICDE 2011, pp. 541–552 (2011)Google Scholar
  13. 13.
    Yao, B., Tang, M., Li, F.: Multi-approximate-keyword routing in GIS data. In: ACM SIGSPATIAL 2011, pp. 201–210 (2011)Google Scholar
  14. 14.
    Zhang, C., Zhang, Y., Zhang, W., Lin, X.: Inverted linear quadtree: efficient top-k spatial keyword search. In: ICDE 2013, pp. 901–912 (2013)Google Scholar
  15. 15.
    Zhang, D., Chee, Y.M., Mondal, A., Tung, A.K.H., Kitsuregawa, M.: Keyword search in spatial databases: towards searching by document. In: ICDE 2009, pp. 688–699Google Scholar
  16. 16.
    Zhang, D., Ooi, B.C., Tung, A.K.H.: Locating mapped resources in web 2.0. In: ICDE 2010, pp. 521–532 (2010)Google Scholar
  17. 17.
    Zhang, L., Sun, X., Hai, Z.: Density-based spatial keyword querying. Future Gener. Comput. Syst. 32(1), 211–221 (2014)CrossRefGoogle Scholar
  18. 18.
    Zheng, K., et al.: Interactive top-k spatial keyword queries. In: ICDE 2015, pp. 423–434 (2015)Google Scholar
  19. 19.
    Zhou, Y., Xie, X., Wang, C., Gong, Y., Ma, W.Y.: Hybrid index structures for location-based web search. In: ACM CIKM 2005, pp. 155–162 (2005)Google Scholar
  20. 20.
    Zobel, J., Moffat, A.: Inverted files for text search engines. ACM Comput. Surv. (2006)Google Scholar
  21. 21.
    Qian, Z., Jiajie, X., Zheng, K., Zhao, P., Zhou, X.: Semantic-aware top-k spatial keyword queries. World Wide Web 21(3), 573–594 (2018)CrossRefGoogle Scholar
  22. 22.
    Sun, J., Xu, J., Zheng, K., Liu, C.: Interactive spatial keyword querying with semantics. In: CIKM 2017, pp. 1727–1736 (2017)Google Scholar
  23. 23.
    Zheng, K.: Interactive top-k spatial keyword queries. In: ICDE 2015, pp. 423–434 (2015)Google Scholar
  24. 24.
    Chen, X., et al.: S2R-tree: a pivot-based indexing structure for semantic-aware spatial keyword search. Geoinformatica (2019).  https://doi.org/10.1007/s10707-019-00372-z
  25. 25.
    Xu, J., Chen, J., Zhou, R., Fang, J., Liu, C.: On workflow aware location-based service composition for personal trip planning. Future Gener. Comput. Syst. (2019).  https://doi.org/10.1016/j.future.2019.03.010CrossRefGoogle Scholar
  26. 26.
    Liu, H., Xu, J., Zheng, K., Liu, C., Du, L., Wu, X.: Semantic-aware query processing for activity trajectories. In: WSDM 2017, pp. 283–292 (2017)Google Scholar
  27. 27.
    Chen, J., Xu, J., Liu, C., Li, Z., Liu, A., Ding, Z.: Multi-objective spatial keyword query with semantics. In: Candan, S., Chen, L., Pedersen, T.B., Chang, L., Hua, W. (eds.) DASFAA 2017. LNCS, vol. 10178, pp. 34–48. Springer, Cham (2017).  https://doi.org/10.1007/978-3-319-55699-4_3CrossRefGoogle Scholar
  28. 28.
    Qian, Z., Xu, J., Zheng, K., Sun, W., Li, Z., Guo, H.: On efficient spatial keyword querying with semantics. In: Navathe, S.B., Wu, W., Shekhar, S., Du, X., Wang, X.S., Xiong, H. (eds.) DASFAA 2016. LNCS, vol. 9643, pp. 149–164. Springer, Cham (2016).  https://doi.org/10.1007/978-3-319-32049-6_10CrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Yun Li
    • 1
    Email author
  • Ziheng Wang
    • 1
  • Jing Chen
    • 1
  • Fei Wang
    • 2
  • Jiajie Xu
    • 1
  1. 1.Soochow UniversitySuzhouChina
  2. 2.Siemens Corporate TechnologySuzhouChina

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