Advertisement

Efficient processing of moving collective spatial keyword queries

  • Hongfei Xu
  • Yu GuEmail author
  • Yu Sun
  • Jianzhong Qi
  • Ge Yu
  • Rui Zhang
Regular Paper
  • 26 Downloads

Abstract

As a major type of continuous spatial queries, the moving spatial keyword queries have been studied extensively. Most existing studies focus on retrieving single objects, each of which is close to the query object and relevant to the query keywords. Nevertheless, a single object may not satisfy all the needs of a user, e.g., a user who is driving may want to withdraw money, wash her car, and buy some medicine, which could only be satisfied by multiple objects. We thereby formulate a new type of queries named the moving collective spatial keyword query (MCSKQ). This type of queries continuously reports a set of objects that collectively cover the query keywords as the query moves. Meanwhile, the returned objects must also be close to the query object and close to each other. Computing the exact result set is an NP-hard problem. To reduce the query processing costs, we propose algorithms, based on safe region techniques, to maintain the exact result set while the query object is moving. We further propose two approximate algorithms to obtain even higher query efficiency with precision bounds. All the proposed algorithms are also applicable to MCSKQ with weighted objects and MCSKQ in the domain of road networks. We verify the effectiveness and efficiency of the proposed algorithms both theoretically and empirically, and the results confirm the superiority of the proposed algorithms over the baseline algorithms.

Keywords

Moving query Collective spatial keyword query Safe region Query processing algorithms 

Notes

Acknowledgements

This work is supported by the National Key R&D Program of China (2018YFB1003404), the National Natural Science Foundation of China (61872070, U1811261), the Fundamental Research Funds for the Central Universities (N171605001) and Liao Ning Revitalization Talents Program (XLYC1807158).

References

  1. 1.
    Guo, L., Shao, J., Aung, H., Tan, K.: Efficient continuous top-\(k\) spatial keyword queries on road networks. Geoinformatica 19(1), 29–60 (2015)Google Scholar
  2. 2.
    Huang, W., Li, G., Tan, K., Feng, J.: Efficient safe-region construction for moving top-\(k\) spatial keyword queries. In: CIKM, pp. 932–941 (2012)Google Scholar
  3. 3.
    Qi, J., Zhang, R., Jensen, C., Ramamohanarao, K., He, J.: Continuous spatial query processing: a survey of safe region based techniques. ACM Comput. Surv. 51(3), 1–39 (2018)CrossRefGoogle Scholar
  4. 4.
    Wu, D., Yiu, M., Jensen, C., Cong, G.: Efficient continuously moving top-\(k\) spatial keyword query processing. In: ICDE, pp. 541–552 (2011)Google Scholar
  5. 5.
    Cao, X., Cong, G., Guo, T., Jensen, C., Ooi, B.: Collective spatial keyword querying. In: SIGMOD, pp. 373–384 (2011)Google Scholar
  6. 6.
    Chan, H., Long, C., Wong, R.: On generalizing collective spatial keyword queries. IEEE Trans. Knowl. Data Eng. 30(9), 1712–1726 (2018)CrossRefGoogle Scholar
  7. 7.
    Long, C., Wong, C., Wang, K., Fu, W.: Collective spatial keyword queries: a distance owner-driven approach. In: SIGMOD, pp. 689–700 (2013)Google Scholar
  8. 8.
    Su, S., Zhao, S., Cheng, X., Bi, R., Cao, X., Wang, J.: Group-based collective keyword querying in road networks. Inf. Process. Lett. 118, 83–90 (2017)MathSciNetCrossRefGoogle Scholar
  9. 9.
    Nutanong, S., Zhang, R., Tanin, E., Kulik, L.: Analysis and evaluation of V*-\(k\)NN: an efficient algorithm for moving \(k\)NN queries. VLDBJ 19(3), 307–332 (2010)Google Scholar
  10. 10.
    Wang, Y., Zhang, R., Xu, C., Qi, J., Gu, Y., Yu, G.: Continuous visible \(k\) nearest neighbor query on moving objects. Inf. Syst. 44, 1–21 (2014)Google Scholar
  11. 11.
    Ward, P., He, Z., Zhang, R., Qi, J.: Real-time continuous intersection joins over large sets of moving objects using graphic processing units. VLDBJ 23(6), 965–985 (2014)CrossRefGoogle Scholar
  12. 12.
    Cao, X., Cong, G., Guo, T., Jensen, C., Ooi, B.: Efficient processing of spatial group keyword queries. ACM TODS 40(2), 1–48 (2015)MathSciNetCrossRefGoogle Scholar
  13. 13.
    Chan, H., Long, C., Wong, R.: Inherent-cost aware collective spatial keyword queries. In: SSTD, pp. 357–375 (2017)Google Scholar
  14. 14.
    Gao, Y., Zhao, J., Zheng, B., Chen, G.: Efficient collective spatial keyword query processing on road networks. IEEE Trans. Intell. Transp. Syst. 17(2), 469–480 (2016)CrossRefGoogle Scholar
  15. 15.
    Jin, X., Shin, S., Jo, E., Lee, K.: Collective keyword query on a spatial knowledge base. IEEE Trans. Knowl. Data Eng. 31(11), 2051–2062 (2019)Google Scholar
  16. 16.
    Zhao, S., Cheng, X., Su, S., Shuang, K.: Popularity-aware collective keyword queries in road networks. Geoinform. 21(3), 485–518 (2017)CrossRefGoogle Scholar
  17. 17.
    Zhang, P., Lin, H., Yao, B., Lu, D.: Level-aware collective spatial keyword queries. Inf. Sci. 378, 194–214 (2017)MathSciNetCrossRefGoogle Scholar
  18. 18.
    Shekhar, S., Liu, D.: Ccam: a connectivity-clustered access method for networks and network computations. IEEE Trans. Knowl. Data Eng. 9(1), 102–119 (1993)CrossRefGoogle Scholar
  19. 19.
    Gu, Y., Liu, G., Qi, J., Xu, H., Yu, G., Zhang, R.: The moving \(k\) diversified nearest neighbor query. IEEE Trans. Knowl. Data Eng. 28(10), 2778–2792 (2016)Google Scholar
  20. 20.
    Li, C., Gu, Y., Qi, J., Yu, G., Zhang, R., Yi, W.: Processing moving \(k\)nn queries using influential neighbor sets. PVLDB 8(2), 113–124 (2014)Google Scholar
  21. 21.
    Tao, Y., Papadias, D., Shen, Q.: Continuous nearest neighbor search. In: VLDB, pp. 287–298 (2002)Google Scholar
  22. 22.
    Attique, M., Cho, H., Jin, R., Chung, T.: Efficient processing of continuous reverse \(k\) nearest neighbor on moving objects in road networks. Geo-Inf 5(12), 247 (2016)Google Scholar
  23. 23.
    Cheema, M., Zhang, W., Lin, X., Zhang, Y., Li, X.: Continuous reverse \(k\) nearest neighbors queries in Euclidean space and in spatial networks. VLDBJ 21(1), 69–95 (2012)Google Scholar
  24. 24.
    Cheema, M., Brankovic, L., Lin, X., Zhang, W., Wang, W.: Multi-guarded safe zone: An effective technique to monitor moving circular range queries. In: ICDE, pp. 189–200 (2010)Google Scholar
  25. 25.
    Cho, H., Ryu, K., Chung, T.: An efficient algorithm for computing safe exit points of moving range queries in directed road networks. Inf. Syst. 41, 1–19 (2014)CrossRefGoogle Scholar
  26. 26.
    Huang, J., Huang, C.: A proxy-based approach to continuous location-based spatial queries in mobile environments. IEEE Trans. Knowl. Data Eng. 25(2), 260–273 (2013)CrossRefGoogle Scholar
  27. 27.
    Mahmood, A., Daghistani, A., Aly, A., Tang, M., Basalamah S., Prabhakar,S., Aref, W.: Adaptive processing of spatial-keyword data over a distributed streaming cluster. In: SIGSPATIAL, pp. 219–228 (2018)Google Scholar
  28. 28.
    Chen, B., Lv, Z., Yu, X., Liu, Y.: Sliding window top-\(k\) monitoring over distributed data streams. Data Sci. Eng. 2(4), 289–300 (2017)Google Scholar
  29. 29.
    Wang, X., Zhang, Y., Zhang, W., Lin, X., Wang, W.: AP-tree: efficiently support location-aware publish/subscribe. VLDBJ 24(6), 823–848 (2015)CrossRefGoogle Scholar
  30. 30.
    Salgado, C., Cheema, M., Ali, M.: Continuous monitoring of range spatial keyword query over moving objects. World Wide Web 21(3), 687–712 (2018)CrossRefGoogle Scholar
  31. 31.
    Guo, L., Zhang, D., Li, G., Tan, K., Bao, Z.: Location-aware pub/sub system: when continuous moving queries meet dynamic event streams. In: SIGMOD, pp. 843–857 (2015)Google Scholar
  32. 32.
    Zheng, B., Zheng, K., Xiao, X., Su, H., Yin, H., Zhou, X., Li, G.: Keyword-aware continuous \(k\)NN query on road networks. In: ICDE, pp. 871–882 (2016)Google Scholar
  33. 33.
    Okabe, A., Boots, B., Sugihara, K., Chiu, S.: Spatial Tessellations: Concepts and Applications of Voronoi Diagrams. Wiley, London (2001)zbMATHGoogle Scholar
  34. 34.
    Liu, C., Papadopoulou, E., Lee, D.: An output-sensitive approach for the L1/L\({\infty }\) \(k\)-nearest-neighbor voronoi diagram. Algorithms ESA 1, 70–81 (2011)Google Scholar
  35. 35.
    Mu, L.: Polygon characterization with the multiplicatively weighted voronoi diagram. Prof. Geogr. 56(2), 223–239 (2004)Google Scholar
  36. 36.
    Kolahdouzan, M., Shahabi, C.: Voronoi-based \(k\) nearest neighbor search for spatial network databases. In: VLDB, pp. 840–851 (2004)Google Scholar
  37. 37.
    Papadias, D., Zhang, J., Mamoulis, N., Tao, Y.: Query processing in spatial network databases. In: VLDB, pp. 802–813 (2003)Google Scholar
  38. 38.
    Chen, L., Cong, G., Cao, X., Tan, K.: Temporal spatial-keyword top-\(k\) publish/subscribe.In: ICDE, pp. 255–266 (2015)Google Scholar
  39. 39.
    Bao, J., Zheng, Y., Mokbel, M.: Location-based and preference-aware recommendation using sparse geo-social networking data. In: SIGSPATIAL, pp. 199–208 (2012)Google Scholar
  40. 40.
    Cong, G., Jensen, C., Wu, D.: Efficient retrieval of the top-\(k\) most relevant spatial web objects. VLDB Endow. 2(1), 337–348 (2009)Google Scholar

Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2019

Authors and Affiliations

  1. 1.College of Computer Science and EngineeringNortheastern UniversityShenyangChina
  2. 2.Twitter, Inc.San FranciscoUSA
  3. 3.The Department of Computing and Information SystemsThe University of MelbourneMelbourneAustralia

Personalised recommendations