Advertisement

Answering Spatial Approximate Keyword Queries in Disks

  • Jinbao WangEmail author
  • Donghua Yang
  • Yuhong Wei
  • Hong Gao
  • Jianzhong Li
  • Ye Yuan
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9313)

Abstract

Spatial approximate keyword queries consist of a spatial condition and a set of keywords as the fuzzy textual conditions, and they return objects labeled with a set of keywords similar to queried keywords while satisfying the spatial condition. Such queries enable users to find objects of interest in a spatial database, and make mismatches between user query keywords and object keywords tolerant. With the rapid growth of data, spatial databases storing objects from diverse geographical regions can be no longer held in main memories. Thus, it is essential to answer spatial approximate keyword queries over disk resident datasets. Existing works present methods either returns incomplete answers or indexes in main memory, and effective solutions in disks are in demand. This paper presents a novel disk resident index RMB-tree to support spatial approximate keyword queries. We study the principle of augmenting R-tree with capacity of approximate keyword searching based on existing solutions, and store multiple bitmaps in R-tree nodes to build an RMB-tree. RMB-tree supports spatial conditions such as range constraint, combined with keyword similarity metrics such as edit distance, dice etc. Experimental results against R-tree on two real world datasets demonstrate the efficiency of our solution.

Keywords

spatial database approximate keyword search index structure query processing 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
  2. 2.
    Chen, L., Jiaheng, L., Yiming, L.: Efficient merging and filtering algorithms for approximate string searches. In: IEEE ICDE 2008 (2008)Google Scholar
  3. 3.
    Chen, Y.-Y., Suel, T., Markowetz, A.: Efficient query processing in geographic web search engines. In: ACM SIGMOD, pp. 277–288 (2006)Google Scholar
  4. 4.
    Guttman, A.: R-trees: A dynamic index structure for spatial searching. In: ACM SIGMOD, pp. 993–1002 (1984)Google Scholar
  5. 5.
    Jensen, C.S., Wu, D., Chen, L., Cong, G.: Spatial keyword query processing: An experimental evaluation. In: VLDB (2013)Google Scholar
  6. 6.
    Marios, H., Amit, C., Nick, K., Divesh, S.: Fast indexes and algorithms for set similarity selection queries. In: IEEE ICDE, pp. 267–276 (2008)Google Scholar
  7. 7.
    Sattam, A., Alexander, B., Li, C.: Supporting location-based approximate-keyword queries. In: ACM SIGSPATIAL, pp. 61–70 (2010)Google Scholar
  8. 8.
    Yao, B., Li, F., Hadjieleftheriou, M., Hou, K.: Approximate string search in spatial databases. In: IEEE ICDE, pp. 545–556 (2010)Google Scholar
  9. 9.
    Yinghua, Z., Xing, X., Chuang, W., Yuchang, G., Wei-Ying, M.: Hybrid index structures for location-based web search. In: ACM CIKM, pp. 155–162 (2005)Google Scholar
  10. 10.
    Zhang, D., Ooi, B.C., Tung, A.: Locating mapped resources in web 2.0. In: IEEE ICDE, pp. 521–532 (2010)Google Scholar
  11. 11.
    Zhang, Z., Hadjieleftheriou, M., Ooi, B.C., Srivastava, D.: Bed-tree: an all-purpose index structure for string similarity search based on edit distance. In: ACM SIGMOD, pp. 915–926 (2010)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  • Jinbao Wang
    • 1
    Email author
  • Donghua Yang
    • 1
  • Yuhong Wei
    • 2
  • Hong Gao
    • 1
  • Jianzhong Li
    • 1
  • Ye Yuan
    • 1
  1. 1.Harbin Institute of TechnologyHarbinChina
  2. 2.ZTE Co. LtdShenzhenChina

Personalised recommendations