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QDR-Tree: An Efficient Index Scheme for Complex Spatial Keyword Query

  • Xinshi Zang
  • Peiwen Hao
  • Xiaofeng Gao
  • Bin Yao
  • Guihai Chen
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11029)

Abstract

With the popularity of mobile devices and the development of geo-positioning technology, location-based services (LBS) attract much attention and top-k spatial keyword queries become increasingly complex.It is common to see that clients issue a query to find a restaurant serving pizza and steak, low in price and noise level particularly.However, most of prior works focused only on the spatial keyword while ignoring these independent numerical attributes.

      In this paper we demonstrate, for the first time, the Attributes-Aware Spatial Keyword Query (ASKQ), and devise a two-layer hybrid index structure called Quad-cluster Dual-filtering R-Tree (QDR-Tree). In the keyword cluster layer, a Quad-Cluster Tree (QC-Tree) is built based on the hierarchical clustering algorithm using kernel k-means to classify keywords.In the spatial layer, for each leaf node of the QC-Tree, we attach a Dual-Filtering R-Tree (DR-Tree) with two filtering algorithms, namely, keyword bitmap-based and attributes skyline-based filtering. Accordingly, efficient query processing algorithms are proposed.

      Through theoretical analysis, we have verified the optimization both in processing time and space consumption. Finally, massive experiments with real-data demonstrate the efficiency and effectiveness of QDR-Tree.

Keywords

Top-k spatial keyword query Skyline algorithm Keyword cluster Location-based service 

References

  1. 1.
    Borzsonyi, S., Stocker, K., Kossmann, D.: The skyline operator. In: IEEE International Conference on 2002 Data Engineering (ICDE), pp. 421–430 (2002)Google Scholar
  2. 2.
    Cao, X., Cong, G., Jensen, C.S.: Retrieving top-k prestige-based relevant spatial web objects. Int. Conf. Very Large Data Bases (VLDB) 3, 373–384 (2010)Google Scholar
  3. 3.
    Chen, L., Gao, Y., Li, X., Jensen, C.S., Chen, G.: Efficient metric indexing for similarity search. IEEE Trans. Knowl. Data Eng. (TKDE) 29(3), 556–571 (2017)CrossRefGoogle Scholar
  4. 4.
    Cong, G., Jensen, C., Wu, D.: Efficient retrieval of the top-k most relevant spatial web objects. Int. Conf. Very Large Data Bases (VLDB) 2(1), 337–348 (2009)Google Scholar
  5. 5.
    Cong, G., Jensen, C.S., Wu, D.: Efficient retrieval of the top-k most relevant spatial web objects. Int. Conf. Very Large Data Bases (VLDB) 2(1), 337–348 (2009)Google Scholar
  6. 6.
    Dhillon, I.S., Guan, Y., Kulis, B.: Kernel k-means: spectral clustering and normalized cuts. In: ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD), pp. 551–556 (2004)Google Scholar
  7. 7.
    Felipe, I.D., Hristidis, V., Rishe, N.: Keyword search on spatial databases. In: IEEE International Conference on Data Engineering (ICDE), pp. 656–665 (2008)Google Scholar
  8. 8.
    Lee, J., Hwang, S.: Toward efficient multidimensional subspace skyline computation. Int. Conf. Very Large Data Bases (VLDB) 23(1), 129–145 (2014)CrossRefGoogle Scholar
  9. 9.
    Li, Z., Lee, K.C., Zheng, B., Lee, W.C., Lee, D., Wang, X.: Ir-tree: an efficient index for geographic document search. IEEE Trans. Knowl. Data Eng. (TKDE) 23(4), 585–599 (2011)CrossRefGoogle Scholar
  10. 10.
    Liu, X., Chen, L., Wan, C.: LINQ: a framework for location-aware indexing and query processing. IEEE Trans. Knowl. Data Eng. (TKDE) 27(5), 1288–1300 (2015)CrossRefGoogle Scholar
  11. 11.
    Ma, G., Arefin, M.S., Morimoto, Y.: A spatial skyline query for a group of users having different positions. In: Third International Conference on Networking and Computing, pp. 137–142 (2012)Google Scholar
  12. 12.
    Mikolov, T., Chen, K., Corrado, G., Dean, J.: Efficient estimation of word representations in vector space. Computer Science (2013)Google Scholar
  13. 13.
    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
  14. 14.
    Qian, Z., Xu, J., Zheng, K., Zhao, P., Zhou, X.: Semantic-aware top-k spatial keyword queries. World Wide Web (WWW), pp. 1–22 (2017)Google Scholar
  15. 15.
    Ray, S., Nickerson, B.G.: Dynamically ranked top-k spatial keyword search. In: ACM International Conference on Management of Data (SIGMOD), pp. 6–18 (2016)Google Scholar
  16. 16.
    Sasaki, Y., Lee, W.-C., Hara, T., Nishio, S.: Sky R-tree: an index structure for distance-based top-k query. In: Bhowmick, S.S., Dyreson, C.E., Jensen, C.S., Lee, M.L., Muliantara, A., Thalheim, B. (eds.) DASFAA 2014. LNCS, vol. 8421, pp. 220–235. Springer, Cham (2014).  https://doi.org/10.1007/978-3-319-05810-8_15CrossRefGoogle Scholar
  17. 17.
    Tao, Y., Sheng, C.: Fast nearest neighbor search with keywords. IEEE Trans. Knowl. Data Eng. (TKDE) 26(4), 878–888 (2014)CrossRefGoogle Scholar
  18. 18.
    Zhang, C., Zhang, Y., Zhang, W., Lin, X.: Inverted linear quadtree: efficient top k spatial keyword search. In: IEEE International Conference on Data Engineering (ICDE), pp. 901–912 (2013)Google Scholar

Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

  • Xinshi Zang
    • 1
  • Peiwen Hao
    • 1
  • Xiaofeng Gao
    • 1
  • Bin Yao
    • 1
  • Guihai Chen
    • 1
  1. 1.Shanghai Key Laboratory of Scalable Computing and Systems, Department of Computer Science and EngineeringShanghai Jiao Tong UniversityShanghaiChina

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