An Efficient Spatio-Textual Skyline Query Processing Algorithm Based on Spark

  • Baiyou QiaoEmail author
  • Jingru Zhang
  • Xiyu Qiao
  • Bing Hu
  • Yujie Zheng
  • Gang Wu
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 1075)


Aiming at the problem of spatio-textual skyline query processing in cloud computing systems, we propose a Spark-based spatio-textual skyline query processing algorithm. In which, the spatial objects irrelevant to query points are filtered out according to the text relevance, and an integration function is used to compute the spatio-textual distances between spatial objects and query points. Then the data space consisting of dynamic spatio-textual distances is divided into same-sized cells by using a grid partitioning method, and the cell dominant relation is used to filter out the cells and related spatial objects, thus reducing the computation cost. A local spatial skyline algorithm is used to compute local skyline results for each cell in parallel, in which, spatial objects having strong dominant capacity are selected as the initial dominating set to further reduce the computing cost and speed up the execution of the algorithm. Experimental results show that the proposed algorithm has good performance and scalability.


Spatio-textual skyline Query processing Spark TF-IDF 



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.
    Borzsony, S., Kossmann, D., Stocker, K.: The skyline operator data engineering. In: International Conference on Data Engineering, pp. 421–430 (2001)Google Scholar
  2. 2.
    Sharifzadeh, M., Shahabi, C.: The spatial skyline queries. In: Proceedings of the International Conference on Very Large Databases, pp. 751–762 (2006)Google Scholar
  3. 3.
    Shi, J., Wu, D., Mamoulis, N.: Textually relevant spatial skylines. IEEE Trans. Knowl. Data Eng. 28, 224–237 (2015)CrossRefGoogle Scholar
  4. 4.
    Kodama, K., Iijima, Y., Guo, X., et al.: Skyline queries based on user locations and preferences for making location-based recommendations. In: International Workshop on LBSN 2009Google Scholar
  5. 5.
    Regalado, A., Goncalves, M., Abad-Mota, S.: Evaluating skyline queries on spatial web objects. DEXA 2012, pp. 416–423 (2012)Google Scholar
  6. 6.
    Li, J., Wang, H., Li, J., et al.: skyline for geo-textual data. Geoinformatica 20(3), 453–469 (2016)CrossRefGoogle Scholar
  7. 7.
    Zhang, J., Jiang, X., Ku, W.S., et al.: Efficient parallel skyline evaluation using MapReduce. IEEE Trans. Parallel Distrib. Syst. 27(7), 1996–2009 (2016)CrossRefGoogle Scholar
  8. 8.
    Sohail, A., Cheema, M.A., Taniar, D.: Social-aware spatial top-k and skyline queries. Comput. J. 61(11), 1620–1638 (2018)MathSciNetGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  • Baiyou Qiao
    • 1
    Email author
  • Jingru Zhang
    • 1
  • Xiyu Qiao
    • 1
  • Bing Hu
    • 1
  • Yujie Zheng
    • 2
  • Gang Wu
    • 1
  1. 1.School of Computer Science and EngineeringNortheastern UniversityShenyangChina
  2. 2.Department of Platform and ArchitectureJD.Com Inc.BeijingChina

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