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潘晓  等:支持 OR 语义的高效受限 Top-k 空间关键字查询技术                                              3213


         6    总   结

             基于位置的地理信息服务在人们的生活中发挥着越来越重要的作用,针对空间文本对象查询的研究成为
         工业界和学术界关注的研究热点问题之一.为了给用户提供更多高品质的选择结果,本文针对基于聚集倒排线
         性四分树的高效 OR 语义的受限 Top-k 空间关键字查询的问题进行了研究.综合考虑空间距离、空间文本相似
         程度的需求,基于聚集倒排线性四分树,分别提出基于虚拟四分树的 VQuad 和基于虚拟网格的 VGrid 算法.两种
         算法均可同时支持 AND 语义和 OR 语义.通过一系列的实验发现,由于 VGrid 直接利用了线性四分树上空间编
         码的特点,在所有的实验设置中其性能均优于 VQuad 且算法性能更稳定.未来考虑将此技术思想应用到在道路
         网络上的查询研究中.


         References:
          [1]    Zhu  H, Yang  X,  Wang  B,  Lee  WC.  Range-based obstructed nearest neighbor queries. In: Proc. of  the  ACM SIGMOD. 2016.
             2053–2068.
          [2]    Li Y, Huang Z, Zhu R, Li G, Shu L, Tian S, Ma M. Parameterized spatio-textual publish/subscribe in rode sensor networks. IEEE
             Access, 2017,5(99):22940–22952.
          [3]    Sanderson M, Kohler J. Analyzing geographic queries. In: Proc. of the Int’l ACM SIGIR Conf. 2004.
          [4]    Chan KH, Long  C,  Wong CW. On  generalizing collective  spatial  keyword  queries. IEEE Trans.  on Knowledge and Data
             Engineering, 2018,30(9):1712–1726.
          [5]    Han XX, Yang DH, Li JZ. TKEP: An efficient top-K query processing algorithm on massive data. Chinese Journal of Computers,
             2010,33(8):1405–1417 (in Chinese with English abstract).
          [6]    Zhu R, Wang B, Yang  X, Zheng B,  Wang G.  SAP:  Improving continuous  top-k  queries over streaming data. IEEE  Trans. on
             Knowledge and Data Engineering, 2017,29(6):1310–1328.
          [7]    Hu HQ, Li GL, Bao ZF, Feng JH, Wu YW, Gong ZG, Xu YQ. Top-k spatio-textual similarity join. IEEE Trans. on Knowledge and
             Data Engineering, 2016,28(2):551–565.
          [8]    Wang Y,  Jian X, Yang ZH, Li J.  Query  optimal  k-plex  based community  in graphs.  Data Science and Engineering,  2017,2(4):
             274–274.
          [9]    Yang J, Zhang Y, et al. Categorical top-k spatial influence query. World Wide Web, 2017,20:175−203.
         [10]    Cong G, Jensen CS, Wu D. Efficient retrieval of the top-k most relevant spatial Web objects. Proc. of the VLDB Endowment, 2009,
             2(1):337–348.
         [11]    Chan KH, Li C. Hybrid indexing and seamless ranking of spatial and textual features of Web documents. In: Proc. of the SSTD.
             2017. 357–375.
         [12]    Li ZS, Lee KC, Zheng BH, Lee WC, Lee D, Wang XF. IR-tree: An efficient index for geographic document search. IEEE Trans. on
             Knowledge and Data Engineering, 2011,23(4):585–599.
         [13]    Rochajunior JB, Gkorgkas O, Jonassen S, Nørvåg K. Efficient processing of top-k spatial keyword queries. In: Proc. of the SSTD.
             2011. 205–222.
         [14]    Liu XP, Wan CX, Liu  DX,  Liao  GQ. Survey on spatial keyword search.  Ruan Jian  Xue  Bao/Journal of Software, 2016,27(2):
             329−347 (in Chinese with English abstract). http://www.org.jos.cn/1000-9825/4934.htm [doi: 10.13328/j.cnki.jos.004934]
         [15]    Zhang D, Tan KL, Tung AKH. Scalable top-k spatial keyword search. In: Proc. of the EDBT. 2013. 359–370.
         [16]    Zhang CY, Zhang Y, Zhang WJ, Lin XM. Inverted linear quadtree: Efficient top k spatial keyword search. In Proc. of the ICDE.
             2013. 901–912.
         [17]    Wu DM, Yiu ML,  Cong G, Jensen CS.  Joint  top-k spatial keyword query processing. IEEE Trans. on  Knowledge  and Data
             Engineering, 2012,24(10):1889–1903.
         [18]    Felipe ID, Hristidis V, Rishe N. Keyword search on spatial databases. In: Proc. of the ICDE. 2008. 656–665.
         [19]    Chen L, Xu JL, Lin X, Jensen CS, Hu HB. Answering why-not spatial keyword top-k queries via keyword adaption. In: Proc. of the
             ICDE. 2016. 697–708.
         [20]    Chan KH, Long C, Wong CW. Inherent-cost aware collective spatial keyword queries. In: Proc. of the SSTD. 2017. 357–375.
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