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Efficient Top K Temporal Spatial Keyword Search

  • Chengyuan Zhang
  • Lei Zhu
  • Weiren Yu
  • Jun LongEmail author
  • Fang Huang
  • Hongbo Zhao
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11154)

Abstract

Massive amount of data that are geo-tagged and associated with text information are being generated at an unprecedented scale in many emerging applications such as location based services and social networks. Due to their importance, a large body of work has focused on efficiently computing various spatial keyword queries. In this paper, we study the top-k temporal spatial keyword query which considers three important constraints during the search including time, spatial proximity and textual relevance. A novel index structure, namely SSG-tree, to efficiently insert/delete spatio-temporal web objects with high rates. Base on SSG-tree an efficient algorithm is developed to support top-k temporal spatial keyword query. We show via extensive experimentation with real spatial databases that our method has increased performance over alternate techniques.

Notes

Acknowledgments

This work was supported in part by the National Natural Science Foundation of China (61702560, 61379110, 61472450), the Key Research Program of Hunan Province (2016JC2018), Natural Science Foundation of Hunan Province (2018JJ3691), and Science and Technology Plan of Hunan Province (2016JC2011).

References

  1. 1.
    Wang, Y., Lin, X., Wu, L., Zhang, W., Zhang, Q.: Exploiting correlation consensus: towards subspace clustering for multi-modal data. In: Proceedings of the ACM International Conference on Multimedia, MM 2014, Orlando, FL, USA, 03–07 November 2014, pp. 981–984 (2014)Google Scholar
  2. 2.
    Wang, Y., Lin, X., Wu, L., Zhang, W.: Effective multi-query expansions: collaborative deep networks for robust landmark retrieval. IEEE Trans. Image Process. 26(3), 1393–1404 (2017)MathSciNetCrossRefGoogle Scholar
  3. 3.
    Wang, Y., Lin, X., Wu, L., Zhang, W., Zhang, Q., Huang, X.: Robust subspace clustering for multi-view data by exploiting correlation consensus. IEEE Trans. Image Process. 24(11), 3939–3949 (2015)MathSciNetCrossRefGoogle Scholar
  4. 4.
    Wang, Y., Zhang, W., Wu, L., Lin, X., Zhao, X.: Unsupervised metric fusion over multiview data by graph random walk-based cross-view diffusion. IEEE Trans. Neural Netw. Learning Syst. 28(1), 57–70 (2017)CrossRefGoogle Scholar
  5. 5.
    Wang, Y., Lin, X., Wu, L., Zhang, W., Zhang, Q.: LBMCH: learning bridging mapping for cross-modal hashing. In: Proceedings of the 38th International ACM SIGIR Conference on Research and Development in Information Retrieval, Santiago, Chile, 9–13 August 2015, pp. 999–1002 (2015)Google Scholar
  6. 6.
    Wu, L., Wang, Y., Li, X., Gao, J.: What-and-where to match: deep spatially multiplicative integration networks for person re-identification. Pattern Recogn. 76, 727–738 (2018)CrossRefGoogle Scholar
  7. 7.
    Wu, L., Wang, Y., Ge, Z., Hu, Q., Li, X.: Structured deep hashing with convolutional neural networks for fast person re-identification. Comput. Vis. Image Underst. 167, 63–73 (2018)CrossRefGoogle Scholar
  8. 8.
    Zhang, C., Zhang, Y., Zhang, W., Lin, X., Cheema, M.A., Wang, X.: Diversified spatial keyword search on road networks. In: Proceedings of the 17th International Conference on Extending Database Technology, EDBT 2014, Athens, Greece, 24–28 March 2014, pp. 367–378 (2014)Google Scholar
  9. 9.
    Christodoulakis, S., Faloutsos, C.: Design considerations for a message file server. IEEE Trans. Softw. Eng. 10(2), 201–210 (1984)CrossRefGoogle Scholar
  10. 10.
    Faloutsos, C., Jagadish, H.V.: Hybrid index organizations for text databases. In: Pirotte, A., Delobel, C., Gottlob, G. (eds.) EDBT 1992. LNCS, vol. 580, pp. 310–327. Springer, Heidelberg (1992).  https://doi.org/10.1007/BFb0032439 CrossRefGoogle Scholar
  11. 11.
    Gargantini, I.: An effective way to represent quadtrees. Commun. ACM 25(12), 905–910 (1982)CrossRefGoogle Scholar
  12. 12.
    Wang, Y., Lin, X., Zhang, Q., Wu, L.: Shifting hypergraphs by probabilistic voting. In: Tseng, V.S., Ho, T.B., Zhou, Z.-H., Chen, A.L.P., Kao, H.-Y. (eds.) PAKDD 2014. LNCS (LNAI), vol. 8444, pp. 234–246. Springer, Cham (2014).  https://doi.org/10.1007/978-3-319-06605-9_20 CrossRefGoogle Scholar
  13. 13.
    Wang, Y., Wu, L.: Beyond low-rank representations: orthogonal clustering basis reconstruction with optimized graph structure for multi-view spectral clustering. Neural Netw. 103, 1–8 (2018)CrossRefGoogle Scholar
  14. 14.
    Zhang, C., Zhang, Y., Zhang, W., Lin, X.: Inverted linear quadtree: efficient top k spatial keyword search. In: 29th IEEE International Conference on Data Engineering, ICDE 2013, Brisbane, Australia, 8–12 April 2013, pp. 901–912 (2013)Google Scholar
  15. 15.
    Zhang, C., Zhang, Y., Zhang, W., Lin, X.: Inverted linear quadtree: efficient top k spatial keyword search. IEEE Trans. Knowl. Data Eng. 28(7), 1706–1721 (2016)CrossRefGoogle Scholar
  16. 16.
    Aref, W.G., Samet, H.: Efficient processing of window queries in the pyramid data structure. In: Proceedings of the Ninth ACM SIGACT-SIGMOD-SIGART Symposium on Principles of Database Systems, 2–4 April 1990, Nashville, Tennessee, USA, pp. 265–272 (1990)Google Scholar
  17. 17.
    Wang, Y., Zhang, W., Wu, L., Lin, X., Fang, M., Pan, S.: Iterative views agreement: an iterative low-rank based structured optimization method to multi-view spectral clustering. In: Proceedings of the Twenty-Fifth International Joint Conference on Artificial Intelligence, IJCAI 2016, New York, NY, USA, 9–15 July 2016, pp. 2153–2159 (2016)Google Scholar
  18. 18.
    Wu, L., Wang, Y., Gao, J., Li, X.: Deep adaptive feature embedding with local sample distributions for person re-identification. Pattern Recogn. 73, 275–288 (2018)CrossRefGoogle Scholar
  19. 19.
    Mouratidis, K., Hadjieleftheriou, M., Papadias, D.: Conceptual partitioning: an efficient method for continuous nearest neighbor monitoring. In: Proceedings of the ACM SIGMOD International Conference on Management of Data, Baltimore, Maryland, USA, 14–16 June 2005, pp. 634–645 (2005)Google Scholar
  20. 20.
    Wang, Y., Lin, X., Zhang, Q.: Towards metric fusion on multi-view data: a cross-view based graph random walk approach. In: 22nd ACM International Conference on Information and Knowledge Management, CIKM 2013, San Francisco, CA, USA, 27 October–1 November 2013, pp. 805–810 (2013)Google Scholar
  21. 21.
    Magdy, A., Mokbel, M.F., Elnikety, S., Nath, S., He, Y.: Mercury: a memory-constrained spatio-temporal real-time search on microblogs. In: IEEE 30th International Conference on Data Engineering, Chicago, ICDE 2014, IL, USA, 31 March–4 April 2014, pp. 172–183 (2014)Google Scholar
  22. 22.
    O’Neil, P.E., Cheng, E., Gawlick, D., O’Neil, E.J.: The log-structured merge-tree (LSM-tree). Acta Inf. 33(4), 351–385 (1996)CrossRefGoogle Scholar
  23. 23.
    Magdy, A., et al.: Taghreed: a system for querying, analyzing, and visualizing geotagged microblogs. In: SIGSPATIAL (2014)Google Scholar

Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

  • Chengyuan Zhang
    • 1
    • 2
  • Lei Zhu
    • 1
    • 2
  • Weiren Yu
    • 3
  • Jun Long
    • 1
    • 2
    Email author
  • Fang Huang
    • 1
  • Hongbo Zhao
    • 4
  1. 1.School of Information ScienceCentral South UniversityChangshaChina
  2. 2.Big Data and Knowledge Engineering InstituteCentral South UniversityChangshaChina
  3. 3.School of Engineering and Applied ScienceAston UniversityBirminghamUK
  4. 4.School of Minerals Processing and BioengineeringCentral South UniversityChangshaChina

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