Efficient Top-k Spatial Distance Joins

  • Shuyao Qi
  • Panagiotis Bouros
  • Nikos Mamoulis
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8098)


Consider two sets of spatial objects R and S, where each object is assigned a score (e.g., ranking). Given a spatial distance threshold ε and an integer k, the top-k spatial distance join (k- SDJ) returns the k pairs of objects, which have the highest combined score (based on an aggregate function γ) among all object pairs in R×S which have spatial distance at most ε. Despite the practical application value of this query, it has not received adequate attention in the past. In this paper, we fill this gap by proposing methods that utilize both location and score information from the objects, enabling top-k join computation by accessing a limited number of objects. Extensive experiments demonstrate that a technique which accesses blocks of data from R and S ordered by the object scores and then joins them using an aR-tree based module performs best in practice and outperforms alternative solutions by a wide margin.


Range Query Spatial Distance Priority Queue Seed Point Spatial Object 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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  1. 1.
    Arge, L., Procopiuc, O., Ramaswamy, S., Suel, T., Vitter, J.S.: Scalable sweeping-based spatial join. In: VLDB, pp. 570–581 (1998)Google Scholar
  2. 2.
    Brinkhoff, T., Kriegel, H.P., Seeger, B.: Efficient processing of spatial joins using R-trees. In: SIGMOD Conference (1993)Google Scholar
  3. 3.
    Chan, E.P.F.: Buffer queries. IEEE Trans. Knowl. Data Eng. 15(4), 895–910 (2003)CrossRefGoogle Scholar
  4. 4.
    Corral, A., Manolopoulos, Y., Theodoridis, Y., Vassilakopoulos, M.: Closest pair queries in spatial databases. In: SIGMOD Conference (2000)Google Scholar
  5. 5.
    Fagin, R., Lotem, A., Naor, M.: Optimal aggregation algorithms for middleware. In: PODS, pp. 102–113 (2001)Google Scholar
  6. 6.
    Hjaltason, G.R., Samet, H.: Incremental distance join algorithms for spatial databases. In: SIGMOD Conference (1998)Google Scholar
  7. 7.
    Hjaltason, G.R., Samet, H.: Distance browsing in spatial databases. ACM Trans. Database Syst. 24(2), 265–318 (1999)CrossRefGoogle Scholar
  8. 8.
    Ilyas, I.F., Aref, W.G., Elmagarmid, A.K.: Supporting top-k join queries in relational databases. In: VLDB, pp. 754–765 (2003)Google Scholar
  9. 9.
    Ilyas, I.F., Beskales, G., Soliman, M.A.: A survey of top-k query processing techniques in relational database systems. ACM Comput. Surv. 40(4) (2008)Google Scholar
  10. 10.
    Leutenegger, S.T., Edgington, J.M., Lopez, M.A.: STR: A simple and efficient algorithm for R-tree packing. In: ICDE, pp. 497–506 (1997)Google Scholar
  11. 11.
    Ljosa, V., Singh, A.K.: Top-k spatial joins of probabilistic objects. In: ICDE, pp. 566–575 (2008)Google Scholar
  12. 12.
    Mamoulis, N., Yiu, M.L., Cheng, K.H., Cheung, D.W.: Efficient top-k aggregation of ranked inputs. ACM Trans. Database Syst. 32(3) (2007)Google Scholar
  13. 13.
    Natsev, A., Chang, Y.C., Smith, J.R., Li, C.S., Vitter, J.S.: Supporting incremental join queries on ranked inputs. In: VLDB, pp. 281–290 (2001)Google Scholar
  14. 14.
    Papadias, D., Kalnis, P., Zhang, J., Tao, Y.: Efficient OLAP operations in spatial data warehouses. In: Jensen, C.S., Schneider, M., Seeger, B., Tsotras, V.J. (eds.) SSTD 2001. LNCS, vol. 2121, pp. 443–459. Springer, Heidelberg (2001)CrossRefGoogle Scholar
  15. 15.
    Roussopoulos, N., Kelley, S., Vincent, F.: Nearest neighbor queries. In: SIGMOD Conference (1995)Google Scholar
  16. 16.
    Schnaitter, K., Polyzotis, N.: Optimal algorithms for evaluating rank joins in database systems. ACM Trans. Database Syst. 35(1) (2010)Google Scholar
  17. 17.
    Shin, H., Moon, B., Lee, S.: Adaptive multi-stage distance join processing. In: SIGMOD Conference (2000)Google Scholar
  18. 18.
    Yiu, M.L., Lu, H., Mamoulis, N., Vaitis, M.: Ranking spatial data by quality preferences. IEEE Trans. Knowl. Data Eng. 23(3), 433–446 (2011)CrossRefGoogle Scholar
  19. 19.
    Zhu, M., Papadias, D., Lee, D.L., Zhang, J.: Top-k spatial joins. IEEE Trans. Knowl. Data Eng. 17(4), 567–579 (2005)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Shuyao Qi
    • 1
  • Panagiotis Bouros
    • 2
  • Nikos Mamoulis
    • 1
  1. 1.Department of Computer ScienceThe University of Hong KongHonk Kong
  2. 2.Department of Computer ScienceHumboldt-Universität zu BerlinGermany

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