World Wide Web

, Volume 20, Issue 4, pp 749–773 | Cite as

Popularity-aware spatial keyword search on activity trajectories

  • Kai Zheng
  • Bolong ZhengEmail author
  • Jiajie Xu
  • Guanfeng Liu
  • An Liu
  • Zhixu Li


The proliferation of GPS-enabled smart mobile devices enables us to collect a large-scale trajectories of moving objects with GPS tags. While the raw trajectories that only consists of positional information have been studied extensively, many recent works have been focusing on enriching the raw trajectories with semantic knowledge. The resulting data, called activity trajectories, embed the information about behaviors of the moving objects and support a variety of applications for better quality of services. In this paper, we propose a Top-k Spatial Keyword (TkSK) query for activity trajectories, with the objective to find a set of trajectories that are not only close geographically but also meet the requirements of the query semantically. Such kind of query can deliver more informative results than existing spatial keyword queries for static objects, since activity trajectories are able to reflect the popularity of user activities and reveal preferable combinations of facilities. However, it is a challenging task to answer this query efficiently due to the inherent difficulties in indexing trajectories as well as the new complexity introduced by the textual dimension. In this work, we provide a comprehensive solution, including the novel similarity function, hybrid indexing structure, efficient search algorithm and further optimizations. Extensive empirical studies on real trajectory set have demonstrated the scalability of our proposed solution.


Popularity Spatial keyword search Activity trajectory 


  1. 1.
    Alvares, L., Bogorny, V., Kuijpers, B., de Macedo, J., Moelans, B., Vaisman, A.: A Model for Enriching Trajectories with Semantic Geographical Information. In: GIS, pp. 1–8 (2007)Google Scholar
  2. 2.
    Cai, Y., Ng, R.: Indexing Spatio-Temporal Trajectories with Chebyshev Polynomials. In: SIGMOD, pp. 599–610 (2004)Google Scholar
  3. 3.
    Cao, X., Cong, G., Jensen, C., Ooi, B.: Collective Spatial Keyword Querying. In: SIGMOD (2011)Google Scholar
  4. 4.
    Cao, X., Cong, G., Jensen, C.S.: Retrieving top-k prestige-based relevant spatial Web objects. Proc. VLDB Endowment 3(1-2), 373–384 (2010)CrossRefGoogle Scholar
  5. 5.
    Chakka, V., Everspaugh, A., Patel, J.: Indexing Large Trajectory Data Sets with Seti. In: CIDR (2003)Google Scholar
  6. 6.
    Chen, L., Özsu, M., Oria, V.: Robust and Fast Similarity Search for Moving Object Trajectories. In: SIGMOD, pp. 491–502 (2005)Google Scholar
  7. 7.
    Christoforaki, M., He, J., Dimopoulos, C., Markowetz, A., Suel, T.: Text Vs. Space: Efficient Geo-Search Query Processing. In: Proceedings of the 20Th ACM International Conference on Information and Knowledge Management, pp. 423–432 (2011)Google Scholar
  8. 8.
    Cong, G., Jensen, C.S., Wu, D.: Efficient retrieval of the top-k most relevant spatial Web objects. Proc. VLDB Endowment 2(1), 337–348 (2009)CrossRefGoogle Scholar
  9. 9.
    Cudre-Mauroux, P., Wu, E., Madden, S.: Trajstore: an Adaptive Storage System for Very Large Trajectory Data Sets. In: ICDE, pp. 109–120 (2010)Google Scholar
  10. 10.
    De Felipe, I., Hristidis, V., Rishe, N.: Keyword Search on Spatial Databases. In: ICDE, pp. 656–665 (2008)Google Scholar
  11. 11.
    Ester, M., Kriegel, H., Sander, J., Xu, X.: A Density-Based Algorithm for Discovering Clusters in Large Spatial Databases with Noise. In: SIGKDD, vol. 96, pp. 226–231 (1996)Google Scholar
  12. 12.
    Frentzos, E., Gratsias, K., Pelekis, N., Theodoridis, Y.: Nearest neighbor search on moving object trajectories. SSTD 328–345 (2005)Google Scholar
  13. 13.
    Giannotti, F., Nanni, M., Pinelli, F., Pedreschi, D.: Trajectory Pattern Mining. In: SIGKDD, pp. 330–339 (2007)Google Scholar
  14. 14.
    Hariharan, R., Hore, B., Li, C., Mehrotra, S.: Processing Spatial-Keyword (Sk) Queries in Geographic Information Retrieval (Gir) Systems. In: SSBDM, pp. 16–25 (2007)Google Scholar
  15. 15.
    Hjaltason, G., Samet, H.: Incremental distance join algorithms for spatial databases. ACM SIGMOD Record 27(2), 237–248 (1998)CrossRefGoogle Scholar
  16. 16.
    Hua,W.,Wang, Z.,Wang, H., Zheng, K., Zhou, X.: Short Text Understanding through Lexical-Semantic Analysis. In: 2015 IEEE 31St International Conference on Data Engineering, pp. 495–506 (2015)Google Scholar
  17. 17.
    Jeung, H., Shen, H., Zhou, X.: Convoy Queries in Spatio-Temporal Databases. In: ICDE, pp. 1457–1459 (2008)Google Scholar
  18. 18.
    Jeung, H., Yiu, M., Zhou, X., Jensen, C., Shen, H.: Discovery of convoys in trajectory databases. Proc. VLDB Endowment 1(1), 1068–1080 (2008)CrossRefGoogle Scholar
  19. 19.
    Lee, J., Han, J., Whang, K.: Trajectory Clustering: a Partition-And-Group Framework. In: SIGMOD, p 604 (2007)Google Scholar
  20. 20.
    Li, Z., Ding, B., Han, J., Kays, R.: Swarm: Mining relaxed temporal moving object clusters. Proc. of the VLDB Endowment 3(1-2), 723–734 (2010)CrossRefGoogle Scholar
  21. 21.
    Li, Z., Ding, B., Han, J., Kays, R., Nye, P.: Mining Periodic Behaviors for Moving Objects. In: SIGKDD, pp. 1099–1108 (2010)Google Scholar
  22. 22.
    Lu, J., Lu, Y., Cong, G.: Reverse Spatial and Textual K Nearest Neighbor Search. In: SIGMOD (2011)Google Scholar
  23. 23.
    Mao, R., Xu, H., Wu, W., Li, J., Li, Y., Lu, M.: Overcoming the challenge of variety: big data abstraction, the next evolution of data management for aal communication systems. IEEE Commun. Mag. 53(1), 42–47 (2015)CrossRefGoogle Scholar
  24. 24.
    Mao, R., Zhang, P., Li, X., Liu, X., Lu, M.: Pivot selection for metric-space indexing. Int. J. Mach. Learn. Cybern. 7(2), 311–323 (2016)CrossRefGoogle Scholar
  25. 25.
    Ni, J., Ravishankar, C.: Indexing spatio-temporal trajectories with efficient polynomial approximations. TKDE 19(5), 663–678 (2007)Google Scholar
  26. 26.
    Pfoser, D., Jensen, C., Theodoridis, Y.: Novel Approaches in Query Processing for Moving Object Trajectories. In: VLDB, pp. 395–406 (2000)Google Scholar
  27. 27.
    Pfoser, D., Jensen, C., Theodoridis, Y.: Novel Approaches to the Indexing of Moving Object Trajectories. In: VLDB, pp. 395–406 (2000)Google Scholar
  28. 28.
    Sefling, R.J.: Approximation theorems of mathematical statistics wiley (1980)Google Scholar
  29. 29.
    Shang, S., Chen, L., Wei, Z., Jensen, C.S., Wen, J.R., Kalnis, P.: Collective travel planning in spatial networks. IEEE Trans. Knowl. Data Eng. 28(5), 1132–1146 (2016)CrossRefGoogle Scholar
  30. 30.
    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)CrossRefGoogle Scholar
  31. 31.
    Shang, S., Liu, J., Zheng, K., Lu, H., Pedersen, T.B., Wen, J.R.: Planning unobstructed paths in traffic-aware spatial networks. GeoInformatica 19(4), 723–746 (2015)CrossRefGoogle Scholar
  32. 32.
    Shang, S., Zheng, K., Jensen, C.S., Yang, B., Kalnis, P., Li, G., Wen, J.R.: Discovery of path nearby clusters in spatial networks. IEEE Trans. Knowl. Data Eng. 27(6), 1505–1518 (2015)CrossRefGoogle Scholar
  33. 33.
    Vlachos, M., Gunopoulos, D., Kollios, G.: Discovering Similar Multidimensional Trajectories. In: ICDE, p 0673 (2002)Google Scholar
  34. 34.
    Wang, H., Su, H., Zheng, K., Sadiq, S., Zhou, X.: An Effectiveness Study on Trajectory Similarity Measures. In: Proceedings of the Twenty-Fourth Australasian Database Conference-Volume 137, Pp. 13–22. Australian Computer Society, Inc (2013)Google Scholar
  35. 35.
    Wang, H., Zheng, K., Xu, J., Zheng, B., Zhou, X., Sadiq, S.: Sharkdb: an In-Memory Column-Oriented Trajectory Storage. In: Proceedings of the 23Rd ACM International Conference on Conference on Information and Knowledge Management, pp. 1409–1418 (2014)Google Scholar
  36. 36.
    Wang, J., Huang, J.Z., Guo, J., Lan, Y.: Recommending high-utility search engine queries via a query-recommending model. Neurocomputing 167, 195–208 (2015)CrossRefGoogle Scholar
  37. 37.
    Wu, D., Yiu, M., Jensen, C., Cong, G.: Efficient Continuously Moving Top-K Spatial Keyword Query Processing. In: ICDE (2011)Google Scholar
  38. 38.
    Xie, K., Deng, K., Zhou, X.: From Trajectories to Activities: a Spatio-Temporal Join Approach. In: International Workshop on Location Based Social Networks, pp. 25–32 (2009)Google Scholar
  39. 39.
    Yan, Z., Chakraborty, D., Parent, C., Spaccapietra, S., Aberer, K.: Semitri: a Framework for Semantic Annotation of Heterogeneous Trajectories. In: EDBT, pp. 259–270 (2011)Google Scholar
  40. 40.
    Yuan, J., Zheng, Y., Xie, X., Sun, G.: T-drive: Enhancing driving directions with taxi drivers’ intelligence. IEEE Trans. Knowl. Data Eng. 25(1), 220–232 (2013)CrossRefGoogle Scholar
  41. 41.
    Zheng, K., Shang, S., Yuan, N.J., Yang, Y.: Towards Efficient Search for Activity Trajectories. In: ICDE (2013)Google Scholar
  42. 42.
    Zheng, K., Su, H., Zheng, B., Shang, S., Xu, J., Liu, J., Zhou, X.: Interactive Top-K Spatial Keyword Queries. In: 2015 IEEE 31St International Conference on Data Engineering, pp. 423–434 (2015)Google Scholar
  43. 43.
    Zheng, Y., Li, Q., Chen, Y., Xie, X., Ma, W.: Understanding Mobility Based on Gps Data. In: International Conference on Ubiquitous Computing, pp. 312–321 (2008)Google Scholar
  44. 44.
    Zheng, Y., Xie, X., Ma, W.Y.: Geolife: a collaborative social networking service among user, location and trajectory. IEEE Data Eng. Bull. 33(2), 32–39 (2010)Google Scholar
  45. 45.
    Zheng, Y., Zhang, L., Xie, X., Ma, W.: Mining Interesting Locations and Travel Sequences from Gps Trajectories. In: WWW, pp. 791–800 (2009)Google Scholar
  46. 46.
    Zhou, Y., Xie, X., Wang, C., Gong, Y., Ma, W.: Hybrid Index Structures for Location-Based Web Search. In: CIKM, pp. 155–162 (2005)Google Scholar
  47. 47.
    Zhu, Z., Xiao, J., Li, J., Wang, F., Zhang, Q.: Global path planning of wheeled robots using multi-objective memetic algorithms. Integrated Comput.-Aided Eng. 22(4), 387–404 (2015)CrossRefGoogle Scholar
  48. 48.
    Zobel, J., Moffat, A.: Inverted files for text search engines. ACM Comput. Surv. 38(2), 1–56 (2006)CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media New York 2016

Authors and Affiliations

  • Kai Zheng
    • 1
  • Bolong Zheng
    • 2
    Email author
  • Jiajie Xu
    • 1
  • Guanfeng Liu
    • 1
  • An Liu
    • 1
  • Zhixu Li
    • 1
  1. 1.School of Computer Science and TechonologySoochow UniversitySuzhouChina
  2. 2.The University of QueenslandBrisbaneAustralia

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