Advertisement

Exploring Bit Arrays for Join Processing in Spatial Data Streams

  • Wendy OsbornEmail author
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 1036)

Abstract

In this paper, the use of bit arrays for processing spatial joins in spatial data streams is explored. Although spatial joins between objects have been explored in other contexts, such as centralized and distributed systems, they have not been explored in great detail in spatial data streams. This work explores the use of a Bloom-filter (i.e., bit array) inspired representation of a spatial object. Strategies for both mapping objects to bit arrays, and processing spatial joins using the bit arrays in a data stream environment are presented. The strategies are evaluated and compared with spatial (non-bit) join approaches. Performance improvements are identified, and areas of improvement are also identified.

References

  1. 1.
    Abel, D., Ooi, B., Tan, K.L., Power, R., Yu, J.: Spatial join strategies in distributed spatial DBMS. In: Proceedings of the 4th International Symposium on Advances in Spatial Databases (1995)CrossRefGoogle Scholar
  2. 2.
    Aji, A., Wang, F., Vo, H., Lee, R., Liu, Q., Zhang, X., Saltz, J.: Hadoop-GIS: a high-performance spatial data warehousing system over MapReduce. Proc. VLDB 6, 1009–1020 (2013)CrossRefGoogle Scholar
  3. 3.
    Arge, L., Procopiuc, O., Ramaswamy, S., Suel, T., Vitter, J.: Scalable sweeping-based spatial join. In: Proceedings of the 24th International Conference on Very Large Databases, pp. 570–581 (1998)Google Scholar
  4. 4.
    Babu, S., Widom, J.: Continuous queries over data streams. SIGMOD Rec. 30(3), 109–120 (2011)CrossRefGoogle Scholar
  5. 5.
    Bloom, B.H.: Space/time trade-offs in hash coding with allowable errors. Commun ACM 13(7), 422–426 (1970)CrossRefGoogle Scholar
  6. 6.
    Farruque, N., Osborn, W.: Efficient distributed spatial semijoins and their application in multiple-site queries. In: Proceedings of the 28th IEEE International Conference on Advanced Information Networking and Applications. IEEE Computer Society (IEEE) (2014)Google Scholar
  7. 7.
    Han, J., Kamber, M., Pei, J.: Data Mining: Concepts and Techniques. Morgan Kaufmann, Boston (2011)zbMATHGoogle Scholar
  8. 8.
    Hua, Y., Xiao, B., Wang, J.: BR-tree: a scalable prototype for supporting multiple queries of multidimensional data. IEEE Trans. Comput. 58(12), 1585–1598 (2009)MathSciNetCrossRefGoogle Scholar
  9. 9.
    Huang, Y.W., Jing, N., Rundensteiner, E.: Integrated query processing strategies for spatial path queries. In: Proceedings of the 13th International Conference on Data Engineering, pp. 477 –486 (1997).  https://doi.org/10.1109/ICDE.1997.582010
  10. 10.
    Jacox, E., Samet, H.: Spatial join techniques. ACM Trans. Database Syst. 32(1), 1–44 (2007). Article No. 7CrossRefGoogle Scholar
  11. 11.
    Kalnis, P., Mamoulis, N., Bakiras, S., Li, X.: Ad-hoc distributed spatial joins on mobile devices. In: Proceedings of the 20th IEEE International Parallel and Distributed Processing Symposium (2006)Google Scholar
  12. 12.
    Kang, M.S., Ko, S.K., Koh, K., Choy, Y.C.: A parallel spatial join processing for distributed spatial databases. In: Proceedings of the 5th International Conference on Flexible Query Answering Systems, FQAS 2002, London, UK, pp. 212–225. Springer (2002). http://portal.acm.org/citation.cfm?id=645424.652610CrossRefGoogle Scholar
  13. 13.
    Karam, O.: Optimizing distributed spatial joins using R-trees. Ph.D. thesis, Tulane University (2001)Google Scholar
  14. 14.
    Karam, O., Petry, F.: Optimizing distributed spatial joins using R-trees. In: Proceedings of the 43rd ACM Southeast Conference (2006)Google Scholar
  15. 15.
    Kwon, O., Li, K.J.: Progressive spatial join for polygon data stream. In: Proceedings of the 19th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems. ACM (2011)Google Scholar
  16. 16.
    Osborn, W., Zaamout, S.: Using spatial semijoins over multiple sites in distributed spatial query processing. Can. J. Electr. Comput. Eng. 39(2), 71–81 (2016)CrossRefGoogle Scholar
  17. 17.
    Patel, J., DeWitt, D.: Partition based spatial-merge join. In: Proceedings of the 1996 ACM SIGMOD International Conference on Management of Data, vol. 25, pp. 259–270 (1996)CrossRefGoogle Scholar
  18. 18.
    Shekhar, S., Chawla, S.: Spatial Databases: A Tour. Prentice Hall, Upper Saddle River (2003)Google Scholar
  19. 19.
    Tan, K.L., Ooi, B., Abel, D.: Exploiting spatial indexes for semijoin-based join processing in distributed spatial databases. IEEE Trans. Knowl. Data Eng. 12(6), 920–937 (2000)CrossRefGoogle Scholar
  20. 20.
    Zhong, Y., Han, J., Zhang, T., Li, Z., Fang, J., Chen, G.: Towards parallel spatial query processing for big spatial data. In: Proceedings of the 26th IEEE International Parallel and Distributed Processing Symposium Workshops, pp. 2085–2094 (2012)Google Scholar
  21. 21.
    Zhou, X., Abel, D., Truffet, D.: Data partitioning for parallel spatial join processing. Geoinformatica 2, 175–204 (1998)CrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  1. 1.University of LethbridgeLethbridgeCanada

Personalised recommendations