An Efficient Top-k Spatial Join Query Processing Algorithm on Big Spatial Data

  • Baiyou QiaoEmail author
  • Bing Hu
  • Xiyu Qiao
  • Laigang Yao
  • Junhai Zhu
  • Gang Wu
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11642)


Based on Spark platform, we propose an efficient top-k spatial join query processing algorithm on big spatial data, in which, the whole data space is divided into same-sized cells by using a grid partitioning method. Then spatial objects in two data sets are projected and replicated to these cells by projection and replication operations respectively, meanwhile a filtering operation is used to speed up the processing. After that, an R-tree based local top-k spatial join algorithm is proposed to compute the top-k candidate results in each cell, which extends the traditional R-tree index and combines threshold filtering techniques to reduce the communication and computation costs, therefore speeding up the query processing. Experimental results on synthetic data sets show that the proposed algorithm is significantly better than the existing top-k spatial join query processing algorithms in performance.


Big spatial data Spark Top-k spatial join query R-tree 



This research was supported by the National Key R&D Program of China (NO. 2016YFC1401900 and 2018YFB1004402) and National Natural Science Foundation of China (No. 61872072 and 61073063).


  1. 1.
    Zhu, M., Papadias, D., Lun Lee, D., Zhang, J.: Top-k spatial joins. IEEE Trans. Knowl. Data Eng. 17(4), 567–579 (2005)CrossRefGoogle Scholar
  2. 2.
    Govindarajan, S., Agarwal, P.K., Arge, L.: CRB-tree: an efficient indexing scheme for range-aggregate queries. In: Calvanese, D., Lenzerini, M., Motwani, R. (eds.) ICDT 2003. LNCS, vol. 2572, pp. 143–157. Springer, Heidelberg (2003). Scholar
  3. 3.
    Tao, Y., Papadias, D.: Range aggregate processing in spatial databases. IEEE Trans. Knowl. Data Eng. 16(12), 1555–1570 (2004)CrossRefGoogle Scholar
  4. 4.
    Ljosa, V., Singh, A.K.: Top-k spatial joins of probabilistic objects. In: Proceedings of the 24th International Conference on Data Engineering, pp. 566–575 (2008)Google Scholar
  5. 5.
    Aji, A., et al.: Hadoop-GIS: a high performance spatial data warehousing system over MapReduce. PVLDB 6(11), 1009–1020 (2013)Google Scholar
  6. 6.
    Eldawy, A., Mokbel, M.F.: Spatialhadoop: a mapreduce framework for spatial data. In: ICDE Conference, pp. 1352–1363 (2015)Google Scholar
  7. 7.
    You, S., Zhang, J., Gruenwald, L.: Large-scale spatial join query processing in cloud. In: ICDE Workshops, pp. 34–41 (2015)Google Scholar
  8. 8.
    Yu, J., Wu, J., Sarwat, M.: Geospark: a cluster computing framework for processing large-scale spatial data. In: SIGSPATIAL Conference, pp. 70:1–70:4 (2015)Google Scholar
  9. 9.
    Tang, M., Yu, Y., Malluhi, Q.M., Ouzzani, M., Aref, W.G.: Locationspark: a distributed in-memory data management system for big spatial data. PVLDB 9(13), 1565–1568 (2016)Google Scholar
  10. 10.
    You-Zhong, M.A., Xiang, C.I., Meng, X.-F.: Parallel top-k join on massive high-dimensional vectors. Chin. J. Comput. 38(1), 86–98 (2015). (in Chinese)Google Scholar
  11. 11.
    Kim, Y., Shim, K.: Parallel top-k similarity join algorithms using MapReduce. In: Proceedings of the 28th International Conference on Data Engineering, pp. 510–521 (2012)Google Scholar
  12. 12.
    Xu, H., Ding, X., Jin, H., Jiang, W.: Parallel top-k, query processing on uncertain strings using MapReduce. In: Proceedings of the 20th International Conference on Database Systems for Advanced Applications, pp. 89–103 (2015)CrossRefGoogle Scholar
  13. 13.
    Liu, Y., Chen, L., Jing, N., Liu, L.: Parallel top-k spatial join query processing on massive spatial data. J. Comput. Res. Dev. 48(1), 163–172 (2011). (in Chinese)Google Scholar
  14. 14.
    Zhang, S., Han, J., Liu, Z., Wang, K., Xu, Z.: SJMR: parallelizing spatial join with Mapreduce on clusters. In: Proceedings of the IEEE International Conference on Cluster Computing, pp. 1–8 (2009)Google Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Baiyou Qiao
    • 1
    Email author
  • Bing Hu
    • 1
  • Xiyu Qiao
    • 1
  • Laigang Yao
    • 1
  • Junhai Zhu
    • 1
  • Gang Wu
    • 1
  1. 1.School of Computer Science and EngineeringNortheastern UniversityShenyangChina

Personalised recommendations