Skip to main content
Log in

Approximate spatio-temporal top-k publish/subscribe

  • Published:
World Wide Web Aims and scope Submit manuscript


Location-based publish/subscribe plays a significant role in mobile information disseminations. In this light, we propose and study a novel problem of processing location-based top-k subscriptions over spatio-temporal data streams. We define a new type of approximate location-based top-k subscription, Approximate Temporal Spatial-Keyword Top-k (ATSK) Subscription, that continuously feeds users with relevant spatio-temporal messages by considering textual similarity, spatial proximity, and information freshness. Different from existing location-based top-k subscriptions, Approximate Temporal Spatial-Keyword Top-k (ATSK) Subscription can automatically adjust the triggering condition by taking the triggering score of other subscriptions into account. The group filtering efficacy can be substantially improved by sacrificing the publishing result quality with a bounded guarantee. We conduct extensive experiments on two real datasets to demonstrate the performance of the developed solutions.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Subscribe and save

Springer+ Basic
EUR 32.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or Ebook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Figure 1
Figure 2
Figure 3
Figure 4
Figure 5
Figure 6
Figure 7
Figure 8
Figure 9
Figure 10
Figure 11
Figure 12

Similar content being viewed by others


  1. Amati, G., Amodeo, G., Gaibisso, C.: Survival analysis for freshness in microblogging search. In: CIKM, pp. 2483–2486. ACM (2012)

  2. Chen, L., Cong, G.: Diversity-aware top-k publish/subscribe for text stream. In: SIGMOD, pp. 347–362 (2015)

  3. Chen, L., Cong, G., Cao, X.: An efficient query indexing mechanism for filtering geo-textual data. In: SIGMOD, pp. 749–760 (2013)

  4. Chen, L., Cong, G., Jensen, C.S., Wu, D.: Spatial keyword query processing: an experimental evaluation. In: PVLDB, pp. 217–228 (2013)

  5. Chen, L., Cui, Y., Cong, G., Cao, X.: SOPS: A system for efficient processing of spatial-keyword publish/subscribe, vol. 7 (2014)

  6. Chen, Z., Cafarella, M.J.: Integrating spreadsheet data via accurate and low-effort extraction. In: KDD, pp. 1126–1135 (2014)

  7. Chen, L., Cong, G., Cao, X., Tan, K.: Temporal spatial-keyword top-k publish/subscribe. In: ICDE, pp. 255–266 (2015)

  8. Chen, Z., Cafarella, M.J., Jagadish, H.V.: Long-tail vocabulary dictionary extraction from the Web. In: WSDM, pp. 625–634 (2016)

  9. Chen, Z., Dadiomov, S., Wesley, R., Xiao, G., Cory, D., Cafarella, M.J., Mackinlay, J.: Spreadsheet property detection with rule-assisted active learning. In: CIKM, pp. 999–1008 (2017)

  10. Christoforaki, M., He, J., Dimopoulos, C., Markowetz, A., Suel, T.: Text vs. space: Efficient geo-search query processing. In: CIKM, pp. 423–432 (2011)

  11. Cong, G., Jensen, C.S., Wu, D.: Efficient retrieval of the top-k most relevant spatial Web objects. In: PVLDB, pp. 337–348 (2009)

  12. Efron, M., Golovchinsky, G.: Estimation methods for ranking recent information. In: SIGIR, pp. 495–504. ACM (2011)

  13. Guo, D., Zhu, Y., Xu, W., Shang, S., Ding, Z.: How to find appropriate automobile exhibition halls: Towards a personalized recommendation service for auto show. Neurocomputing 213, 95–101 (2016)

    Article  Google Scholar 

  14. Haghani, P., Michel, S., Aberer, K.: Evaluating top-k queries over incomplete data streams. In: CIKM, pp. 877–886 (2009)

  15. Haghani, P., Michel, S., Aberer, K.: The gist of everything new: Personalized top-k processing over Web 2.0 streams. In: CIKM, pp. 489–498 (2010)

  16. Han, J., Zheng, K., Sun, A., Shang, S., Wen, J.: Discovering neighborhood pattern queries by sample answers in knowledge base. In: ICDE, pp. 1014–1025 (2016)

  17. Hu, S., Wen, J., Dou, Z., Shang, S.: Following the dynamic block on the Web. World Wide Web 19(6), 1077–1101 (2016)

    Article  Google Scholar 

  18. Felipe, I.D., Hristidis, V, Rishe, N.: Keyword search on spatial databases. In: ICDE, pp. 656–665 (2008)

  19. Li, X., Croft, W.B.: Time-based language models. In: CIKM, pp. 469–475. ACM (2003)

  20. Li, G., Wang, Y., Wang, T., Feng, J.: Location-aware publish/subscribe. In: KDD, pp. 802–810 (2013)

  21. Li, Z., Shang, S., Xie, Q., Zhang, X.: Cost reduction for Web-based data imputation. In: DASFAA, pp. 438–452 (2014)

  22. Liang, H., Xu, Y., Tjondronegoro, D., Christen, P.: Time-aware topic recommendation based on micro-blogs. In: CIKM, pp. 1657–1661 (2012)

  23. Liu, K., Yang, B., Shang, S., Li, Y., Ding, Z: MOIR/UOTS: Trip recommendation with user oriented trajectory search. In: MDM, pp. 335–337 (2013)

  24. Liu, K., Li, Y., Dai, J., Shang, S., Zheng, K.: Compressing large scale urban trajectory data. In: CloudDP@EuroSys, pp. 3:1–3:6 (2014)

  25. Liu, K., Li, Y., Ding, Z., Shang, S., Zheng, K.: Benchmarking big data for trip recommendation. In: ICCCN, pp. 1–6 (2014)

  26. Liu, J., Zhao, K., Sommer, P., Shang, S., Kusy, B., Jurdak, R.: Bounded quadrant system: Error-bounded trajectory compression on the go. In: ICDE, pp. 987–998 (2015)

  27. Liu, J., Shang, S., Zheng, K., Wen, J.: Multi-view ensemble learning for dementia diagnosis from neuroimaging: An artificial neural network approach. Neurocomputing 195, 112–116 (2016)

    Article  Google Scholar 

  28. Liu, J., Zhao, K., Sommer, P., Shang, S., Kusy, B., Lee, J., Jurdak, R.: A novel framework for online amnesic trajectory compression in resource-constrained environments. IEEE Trans. Knowl Data Eng. 28(11), 2827–2841 (2016)

    Article  Google Scholar 

  29. Liu, A., Wang, W., Shang, S., Li, Q., Zhang, X.: Efficient task assignment in spatial crowdsourcing with worker and task privacy protection. GeoInformatica, online first, 1–28 (2017)

  30. Liu, A., Shen, X., Li, Z., Xu, J., Zhao, L., Zheng, K., Shang, S.: Differential private collaborative Web services qos prediction. World Wide Web, online first, 1–25 (2018)

  31. Machanavajjhala, A., Vee, E., Garofalakis, M., Shanmugasundaram, J.: Scalable ranked publish/subscribe. PVLDB 1(1), 451–462 (2008)

    Google Scholar 

  32. Pripužić, K., žarko, I.P., Aberer, K.: Top-k/w publish/subscribe: finding k most relevant publications in sliding time window w. In: DEBS, pp. 127–138 (2008)

  33. Rocha-Junior, J.B., Gkorgkas, O., Jonassen, S., Nørvåg, K.: Efficient processing of top-k spatial keyword queries. In: SSTD, pp. 205–222 (2011)

  34. Shang, S., Yuan, B., Deng, K., Xie, K., Zhou, X.: Finding the most accessible locations: Reverse path nearest neighbor query in road networks. In: ACM SIGSPATIAL, pp. 181–190 (2011)

  35. Shang, S., Yuan, B., Deng, K., Xie, K., Zheng, K., Zhou, X: PNN query processing on compressed trajectories. GeoInformatica 16(3), 467–496 (2012)

    Article  Google Scholar 

  36. Shang, S., Lu, H., Pedersen, T.B., Xie, X.: Finding traffic-aware fastest paths in spatial networks. In: SSTD, pp. 128–145 (2013)

  37. Shang, S., Lu, H., Pedersen, T.B., Xie, X.: Modeling of traffic-aware travel time in spatial networks. In: MDM, pp. 247–250 (2013)

  38. Shang, S., Ding, R., Zheng, K., Jensen, C.S., Kalnis, P., Zhou, X.: Personalized trajectory matching in spatial networks. VLDB J. 23(3), 449–468 (2014)

    Article  Google Scholar 

  39. Shang, S., Liu, J., Zheng, K., Lu, H., Pedersen, T.B., Wen, J.: Planning unobstructed paths in traffic-aware spatial networks. GeoInformatica 19(4), 723–746 (2015)

    Article  Google Scholar 

  40. Shang, S., Zheng, K., Jensen, C.S., Yang, B., Kalnis, P., Li, G., Wen, J.: Discovery of path nearby clusters in spatial networks. IEEE Trans Knowl Data Eng 27(6), 1505–1518 (2015)

    Article  Google Scholar 

  41. Shang, S., Chen, L., Wei, Z., Guo, D., Wen, J.: Dynamic shortest path monitoring in spatial networks. J Comput Sci Technol 31(4), 637–648 (2016)

    Article  Google Scholar 

  42. Shang, S., Chen, L., Wei, Z., Jensen, C.S., Wen, J., Kalnis, P: Collective travel planning in spatial networks. IEEE Trans. Knowl Data Eng 28(5), 1132–1146 (2016)

    Article  Google Scholar 

  43. Shang, S., Guo, D., Liu, J., Wen, J.: Prediction-based unobstructed route planning. Neurocomputing 213, 147–154 (2016)

    Article  Google Scholar 

  44. Shang, S., Guo, D., Liu, J., Zheng, K., Wen, J.: Finding regions of interest using location based social media. Neurocomputing 173, 118–123 (2016)

    Article  Google Scholar 

  45. Shang, S., Chen, L., Wei, Z., Jensen, C.S., Zheng, K., Kalnis, P.: Trajectory similarity join in spatial networks. PVLDB 10(11), 1178–1189 (2017)

    Google Scholar 

  46. Shang, S., Chen, L., Jensen, C.S., Wen, J., Kalnis, P.: Searching trajectories by regions of interest. IEEE Trans. Knowl Data Eng. 29(7), 1549–1562 (2017)

    Article  Google Scholar 

  47. Shang, S., Zhu, S., Guo, D., Lu, M.: Discovery of probabilistic nearest neighbors in traffic-aware spatial networks. World Wide Web 20(5), 1135–1151 (2017)

    Article  Google Scholar 

  48. Shang, S, Chen, L, Wei, Z, Jensen, CS, Zheng, K., Kalnis, P: Parallel trajectory similarity joins in spatial networks. VLDB J, online first, 1–25 (2018)

  49. Shraer, A., Gurevich, M., Fontoura, M., Josifovski, V.: Top-k publish-subscribe for social annotation of news. PVLDB 6(6), 385–396 (2013)

    Google Scholar 

  50. Wang, X., Zhang, Y., Zhang, W., Lin, X., Wang, W.: Ap-tree: Efficiently support continuous spatial-keyword queries over stream. In: ICDE, pp. 1107–1118 (2015)

  51. Wang, Y., Li, J., Zhong, Y., Zhu, S., Guo, D., Shang, S.: Discovery of accessible locations using region-based geo-social data. WWW J, online first, pp. 1–18 (2018)

  52. Wei, Z., Liu, X., Li, F., Shang, S., Du, X., Wen, J.: Matrix sketching over sliding windows. In: SIGMOD, pp. 1465–1480 (2016)

  53. Wei, Z., He, X., Xiao, X., Wang, S., Shang, S., J. W. e. n.: Topppr: top-k personalized pagerank queries with precision guarantees on large graphs. In: SIGMOD, pp. 1–16 (2018)

  54. Wu, D., Yiu, M.L., Jensen, C.S., Cong, G.: Efficient continuously moving top-k spatial keyword query processing. In: ICDE, pp. 541–552 (2011)

  55. Xie, K., Deng, K., Shang, S., Zhou, X., Zheng, K.: Finding alternative shortest paths in spatial networks. ACM Trans. Database Syst. 37(4), 29:1–29:31 (2012)

    Article  Google Scholar 

  56. Xie, Q., Shang, S., Yuan, B., Pang, C., Zhang, X.: Local correlation detection with linearity enhancement in streaming data. In: CIKM, pp. 309–318 (2013)

  57. Xie, X., Lu, H., Chen, J., Shang, S.: Top-k neighborhood dominating query. In: DASFAA, pp. 131–145 (2013)

  58. Yang, B., Guo, C., Jensen, C.S., Kaul, M., Shang, S.: Stochastic skyline route planning under time-varying uncertainty. In: ICDE, pp. 136–147 (2014)

  59. Yao, B., Chen, Z., Gao, X., Shang, S., Ma, S., Guo, M.: Flexible aggregate nearest neighbor queries in road networks. In: ICDE, pp. 1–12 (2018)

  60. Yao, B., Zheng, W., Wang, Z., Chen, Z., Shang, S., Zheng, K., Guo, M.: Distributed in-memory analytics for big temporal data. In: DASFAA, pp. 1–16 (2018)

  61. Zhang, C., Zhang, Y., Zhang, W., Lin, X.: Inverted linear quadtree: Efficient top k spatial keyword search. In: ICDE, pp. 901–912 (2013)

  62. Zhang, D., Tan, K. -L., Tung, A.K.H.: Scalable top-k spatial keyword search. In: EDBT, pp. 359–370 (2013)

  63. Zheng, K., Zheng, Y., Yuan, N.J., Shang, S.: On discovery of gathering patterns from trajectories. In: ICDE, pp. 242–253 (2013)

  64. Zheng, B., Wang, H., Zheng, K., Su, H., Liu, K., Shang, S.: Sharkdb: An in-memory column-oriented storage for trajectory analysis. World Wide Web 21(2), 455–485 (2018)

    Article  Google Scholar 

  65. Zhu, S., Wang, Y., Shang, S., Zhao, G., Wang, J.: Probabilistic routing using multimodal data. Neurocomputing 253, 49–55 (2017)

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations


Corresponding author

Correspondence to Shuo Shang.

Additional information

This article belongs to the Topical Collection: Special Issue on Big Data Management and Intelligent Analytics

Guest Editors: Junping Du, Panos Kalnis, Wenling Li, and Shuo Shang

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Chen, L., Shang, S. Approximate spatio-temporal top-k publish/subscribe. World Wide Web 22, 2153–2175 (2019).

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: