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

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


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.


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


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Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

  • Xinshi Zang
    • 1
  • Peiwen Hao
    • 1
  • Xiaofeng Gao
    • 1
    Email author
  • 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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