Maintaining Boolean Top-K Spatial Temporal Results in Publish-Subscribe Systems

  • Maryam Ghafouri
  • Xiang Wang
  • Long Yuan
  • Ying Zhang
  • Xuemin Lin
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10837)

Abstract

Nowadays many devices and applications in social networks and location-based services are producing, storing and using description, location and occurrence time of objects. Given a massive number of boolean top-k spatial-temporal queries and the spatial-textual message streams, in this paper we study the problem of continuously updating top-k messages with the highest ranks, each of which contains all the requested keywords when rank of a message is calculated by its location and freshness. Decreasing the ranks of existing top-k results over time and producing new incoming messages, cause continuously computing and maintaining the best results. To the best of our knowledge, there is no prior work that can exactly solve this problem. We propose two indexing and matching methods, then conduct an experimental evaluation to show the impact of parameters and analyse the models.

References

  1. 1.
    Chen, L., Cong, G., Cao, X.: An efficient query indexing mechanism for filtering geo-textual data. In: Proceedings of the 2013 ACM SIGMOD International Conference on Management of Data, pp. 749–760 (2013)Google Scholar
  2. 2.
    Chen, L., Cong, G., Cao, X., Tan, K.L.: Temporal spatial-keyword top-k publish/subscribe. In: Proceedings - International Conference on Data Engineering, pp. 255–266 (2015)Google Scholar
  3. 3.
    Chen, L., Cong, G., Jensen, C., Wu, D.: Spatial keyword query processing: an experimental evaluation. PVLDB 6(3), 217–228 (2013)Google Scholar
  4. 4.
    Choudhury, F.M., Culpepper, J.S., Sellis, T.: Batch processing of top-k spatial-textual queries. In: Second International ACM Workshop on Managing and Mining Enriched Geo-Spatial Data, pp. 7–12 (2015)Google Scholar
  5. 5.
    Christoforaki, M., He, J., Dimopoulos, C., Markowetz, A., Suel, T.: Text vs. space: efficient geo-search query processing. In: 20th ACM International Conference on Information and Knowledge Management, pp. 423–432 (2011)Google Scholar
  6. 6.
    Efron, M., Golovchinsky, G.: Estimation methods for ranking recent information. In: Proceedings of the 34th International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 495–504. ACM (2011)Google Scholar
  7. 7.
    Guo, L., Zhang, D., Li, G., Tan, K.-L., Bao, Z.: Location-aware pub/sub system: when continuous moving queries meet dynamic event streams. In: SIGMOD, pp. 843–857 (2015)Google Scholar
  8. 8.
    Hmedeh, Z., Kourdounakis, H., Christophides, V., du Mouza, C., Scholl, M., Travers, N.: Content-based publish/subscribe system for web syndication. J. Comput. Sci. Technol. 31(2), 359–380 (2016)MathSciNetCrossRefGoogle Scholar
  9. 9.
    Ray, S., Nickerson, B.G.: Dynamically ranked top-k spatial keyword search. In: Proceedings of the Third International ACM SIGMOD Workshop on Managing and Mining Enriched Geo-Spatial Data (2016)Google Scholar
  10. 10.
    Samet, H.: The quadtree and related hierarchical data structures. ACM Comput. Surv. 16(2), 187–260 (1984)MathSciNetCrossRefGoogle Scholar
  11. 11.
    Wang, X., Zhang, Y., Zhang, W., Lin, X., Huang, Z.: SKYPE: top-k spatial-keyword publish/subscribe over sliding window. Proc. VLDB Endow. 9(7), 588–599 (2016)CrossRefGoogle Scholar
  12. 12.
    Wang, X., Zhang, Y., Zhang, W., Lin, X., Wang, W.: AP-tree: efficiently support continuous spatial-keyword queries over stream. In: 31st International Conference on Data Engineering, pp. 1107–1118. IEEE (2015)Google Scholar
  13. 13.
    Yu, M.: A cost-based method for location-aware publish/subscribe services. CIKM 1, 693–702 (2015)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  • Maryam Ghafouri
    • 1
  • Xiang Wang
    • 1
  • Long Yuan
    • 1
  • Ying Zhang
    • 1
    • 2
  • Xuemin Lin
    • 1
  1. 1.The University of New South WalesSydneyAustralia
  2. 2.The University of TechnologySydneyAustralia

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