Multimedia Tools and Applications

, Volume 78, Issue 21, pp 30221–30240 | Cite as

Research of adaptive index based on slide window for spatial-textual query

  • Ling Yuan
  • MingLi Wang
  • HongJu ChengEmail author


Aiming at the spatial-textual continuous query with timeliness, we designed an Adaptive Index based on Slide Window, named as SWSA-Tree, which is composed of a slide window to deal with the time factor and a multi-way Self-Adjust Tree (named as SA-Tree) to index queries adaptively by their spatial and textual information. Based on the proposed index structure, we designed a spatial-textual continuous query algorithm. The experiments were conducted to compare the performance among the proposed SWSA-Tree, the index based on the quadtree and Inverted File (named as QIF) and IQ-tree. The comparison results demonstrated that the proposed adaptive index based on the slide window and the spatial-textual continuous query algorithm were with good scalability and low matching cost.


Spatial-textual object Index structure Slide window Self-adaptive Adaptive cost model 



This work was supported by National Natural Science Fund of China under grants 61502185 and 61370210, the Fundamental Research Funds for the Central Universities (No: 2017KFYXJJ071).


  1. 1.
    Cary A, Wolfson O, Rishe N (2010) Efficient and scalable method for processing top-k spatial boolean queries. In: Scientific and statistical database management. Berlin: Springer Berlin Heidelberg 87–95Google Scholar
  2. 2.
    Chen YY, Suel T, Markowetz A (2006) Efficient query processing in geographic web search engines. In: Proceedings of the 2006 ACM SIGMOD international conference on Management of data. New York: ACM 277–288Google Scholar
  3. 3.
    Chen L, Cong G, Jensen CS et al (2013) Spatial keyword query processing: an experimental evaluation. Proc VLDB Endowment 6(3):217–228CrossRefGoogle Scholar
  4. 4.
    Chen L, Cong G, Cao X (2013) An efficient query indexing mechanism for filtering geo-textual data. In: Proceedings of the 2013 ACM SIGMOD International Conference on Management of Data pp 749–760 ACM, New YorkGoogle Scholar
  5. 5.
    Christoforaki M, He J, Dimopoulos C et al (2011) Text vs. space: efficient geo-search query processing. In: Proceedings of the 20th ACM international conference on Information and knowledge management. New York: ACM 423–432Google Scholar
  6. 6.
    Cong G, Jensen CS, Wu D (2009) Efficient retrieval of the top-k most relevant spatial web objects. Proc VLDB Endowment 2(1):337–348CrossRefGoogle Scholar
  7. 7.
    De Felipe I, Hristidis V, Rishe N (2008) Keyword search on spatial databases. In: 2008 IEEE 24th International Conference on Data Engineering. New York: IEEE 656–665Google Scholar
  8. 8.
    Hariharan R, Hore B, Li C et al (2007) Processing spatial-keyword (SK) queries in geographic information retrieval (GIR) systems. In: 19th International Conference on Scientific and Statistical Database Management. New York: IEEE 16–16Google Scholar
  9. 9.
    Khodaei A, Shahabi C, Li C (2010) Hybrid indexing and seamless ranking of spatial and textual features of web documents. In: Database and expert systems applications. Berlin: Springer Berlin Heidelberg 450–466Google Scholar
  10. 10.
    Li Z, Lee KCK, Zheng B et al (2011) Ir-tree: an efficient index for geographic document search. IEEE Trans Knowl Data Eng 23(4):585–599CrossRefGoogle Scholar
  11. 11.
    Rocha-Junior JB, Gkorgkas O, Jonassen S et al (2011) Efficient processing of top-k spatial keyword queries. Adv Spat Temporal Databases 6849:205–222CrossRefGoogle Scholar
  12. 12.
    Swami A (1989) Optimization of large join queries: combining heuristics and combinatorial techniques. ACM SIGMOD Rec 18(2):367–376CrossRefGoogle Scholar
  13. 13.
    Vaid S, Jones CB, Joho H et al (2005) Spatio-textual indexing for geographical search on the web. In: Advances in spatial and temporal databases. Berlin: Springer Berlin Heidelberg 218–235CrossRefGoogle Scholar
  14. 14.
    Wu D, Cong G, Jensen CS (2012) A framework for efficient spatial web object retrieval. VLDB J Very Large Data Bases 21(6):797–822CrossRefGoogle Scholar
  15. 15.
    Wu D, Yiu ML, Cong G et al (2012) Joint top-k spatial keyword query processing. IEEE Trans Knowl Data Eng 24(10):1889–1903CrossRefGoogle Scholar
  16. 16.
    Yan TW, Garcia-Molina H (1999) The SIFT information dissemination system. ACM Trans Database Syst (TODS) 24(4):529–565CrossRefGoogle Scholar
  17. 17.
    Zhou Y, Xie X, Wang C et al (2005) Hybrid index structures for location-based web search. In: Proceedings of the 14th ACM international conference on Information and knowledge management. New York: ACM 155–162Google Scholar

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© Springer Science+Business Media, LLC, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Huazhong University of Science and TechnologyWuhanChina
  2. 2.College of Mathematics and Computer ScienceFuzhou UniversityFuzhouChina
  3. 3.Key Laboratory of Spatial Data Mining and Information SharingMinistry of EducationFuzhouChina

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