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Region-aware Top-k Similarity Search

  • Sitong LiuEmail author
  • Jianhua Feng
  • Yongwei Wu
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9098)

Abstract

Location-based services have attracted significant attention for the ubiquitous smartphones equipped with GPS systems. These services (e.g., Google map, Twitter) generate large amounts of spatio-textual data which contain both geographical location and textual description. Existing location-based services (LBS) assume that the attractiveness of a Point-of-Interest (POI) depends on its spatial proximity from people. However, in most cases, POIs within a certain distance are all acceptable to users and people may concern more about other aspects. In this paper, we study a region-aware top-k similarity search problem: given a set of spatio-textual objects, a spatial region and several input tokens, finds k most textual-relevant objects falling in this region. We summarize our main contributions as follows: (1) We propose a hybrid-landmark index which integrates the spatial and textual pruning seamlessly. (2) We explore a priority-based algorithm and extend it to support fuzzy-token distance. (3) We devise a cost model to evaluate the landmark quality and propose a deletion-based method to generate high quality landmarks (4) Extensive experiments show that our method outperforms state-of-the-art algorithms and achieves high performance.

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References

  1. 1.
    Alsubaiee, S., Behm, A., Li, C.: Supporting location-based approximate-keyword queries. In: GIS, pp. 61–70 (2010)Google Scholar
  2. 2.
    Bayardo, R.J., Ma, Y., Srikant, R.: Scaling up all pairs similarity search. In: WWW, pp. 131–140 (2007)Google Scholar
  3. 3.
    Chen, L., Cong, G., Jensen, C.S., Wu, D.: Spatial keyword query processing: An experimental evaluation. PVLDB 6(3), 217–228 (2013)Google Scholar
  4. 4.
    Cong, G., Jensen, C.S., Wu, D.: Efficient retrieval of the top-k most relevant spatial web objects. PVLDB 2(1), 337–348 (2009)Google Scholar
  5. 5.
    Deng, D., Li, G., Feng, J., Li, W.-S.: Top-k string similarity search with edit-distance constraints. In: ICDE, pp. 925–936 (2013)Google Scholar
  6. 6.
    Fagin, R., Lotem, A., Naor, M.:. Optimal aggregation algorithms for middleware. In: Proceedings of the Twentieth ACM (2001)Google Scholar
  7. 7.
    Felipe, I.D., Hristidis, V., Rishe, N.: Keyword search on spatial databases. In: ICDE, pp. 656–665 (2008)Google Scholar
  8. 8.
    Guttman, A.: R-trees: A dynamic index structure for spatial searching. In: SIGMOD Conference, pp. 47–57 (1984)Google Scholar
  9. 9.
    Li, C., Lu, J., Lu, Y.: Efficient merging and filtering algorithms for approximate string searches. In: ICDE, pp. 257–266 (2008)Google Scholar
  10. 10.
    Wang, J., Li, G., Feng, J.: Fast-join: An efficient method for fuzzy token matching based string similarity join. In: ICDE, pp. 458–469 (2011)Google Scholar
  11. 11.
    Wang, X., Ding, X., Tung, A.K.H., Zhang, Z.: Efficient and effective knn sequence search with approximate n-grams. PVLDB 7(1), 1–12 (2013)Google Scholar
  12. 12.
    Xiao, C., Wang, W., Lin, X., Shang, H.: Top-k set similarity joins. In: ICDE, pp. 916–927 (2009)Google Scholar
  13. 13.
    Yang, Z., Yu, J., Kitsuregawa, M.: Fast algorithms for top-k approximate string matching. In: AAAI (2010)Google Scholar
  14. 14.
    Yao, B., Li, F., Hadjieleftheriou, M., Hou, K.: Approximate string search in spatial databases. In: ICDE, pp. 545–556 (2010)Google Scholar
  15. 15.
    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: SIGMOD Conference, pp. 915–926 (2010)Google Scholar
  16. 16.
    Zhou, Y., Xie, X., Wang, C., Gong, Y., Ma, W.: Hybrid index structures for location-based web search. In: CIKM, pp. 155–162 (2005)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  1. 1.Department of Computer Science and TechnologyTsinghua UniversityBeijingChina

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