World Wide Web

, Volume 22, Issue 3, pp 967–1000 | Cite as

Searching activity trajectory with keywords

  • Bolong Zheng
  • Kai ZhengEmail author
  • Peter Scheuermann
  • Xiaofang Zhou
  • Quoc Viet Hung Nguyen
  • Chenliang Li
Part of the following topical collections:
  1. Special Issue on Geo-Social Computing


Driven by the advances in location positioning techniques and the popularity of location sharing services, semantic enriched trajectory data has become unprecedentedly available. While finding relevant Point-of-Interests (PoIs) based on users’ locations and query keywords has been extensively studied in the past years, it is, however, largely untouched to explore the keyword queries in the context of activity trajectory database. In this paper, we study the problem of searching activity trajectories by keywords. Given a set of query keywords, a keyword-oriented query for activity trajectory (KOAT) returns k trajectories that contain the most relevant keywords to the query and yield the least travel effort in the meantime. The main difference between KOAT and conventional spatial keyword queries is that there is no query location in KOAT, which means the search area cannot be localized. To capture the travel effort in the context of query keywords, a novel score function, called spatio-textual ranking function, is first defined. Then we develop a hybrid index structure called GiKi to organize the trajectories hierarchically, which enables pruning the search space by spatial and textual similarity simultaneously. Finally an efficient search algorithm and fast evaluation of the value of spatio-textual ranking function are proposed. In addition, we extend the proposed techniques of KOAT to support range-based query and order sensitive query, which can be applied for more practical applications. The results of our empirical studies based on real check-in datasets demonstrate that our proposed index and algorithms can achieve good scalability.


Query processing Spatial keyword query Activity trajectory 



This work is supported by the National Natural Science Foundation of China (Grant No. 61502324, Grant No. 61532018, Grant No. 61572335).


  1. 1.
    Agrawal, R., Faloutsos, C., Swami, A.: Efficient similarity search in sequence databases. Springer, New York (1993)CrossRefGoogle Scholar
  2. 2.
    Behm, A., Ji, S., Li, C., Lu, J.: Space-constrained gram-based indexing for efficient approximate string search. In: ICDE (2009)Google Scholar
  3. 3.
    Cai, Y., Ng, R.: Indexing spatio-temporal trajectories with chebyshev polynomials. In: SIGMOD (2004)Google Scholar
  4. 4.
    Cao, X., Cong, G., Jensen, C.S.: Retrieving top-k prestige-based relevant spatial Web objects. PVLDB (2010)Google Scholar
  5. 5.
    Cao, X., Cong, G., Jensen, C.S., Ooi, B.C.: Collective spatial keyword querying. In: SIGMOD (2011)Google Scholar
  6. 6.
    Chen, L., Ng, R.: On the marriage of lp-norms and edit distance. In: PVLDB, pp. 792–803. VLDB Endowment (2004)Google Scholar
  7. 7.
    Chen, L., Özsu, M.T., Oria, V.: Robust and fast similarity search for moving object trajectories. In: SIGMOD, pp. 491–502. ACM (2005)Google Scholar
  8. 8.
    Chen, Z., Shen, H.T., Zhou, X., Zheng, Y., Xie, X.: Searching trajectories by locations: an efficiency study. In: SIGMOD, pp. 255–266. ACM (2010)Google Scholar
  9. 9.
    Chen, L., Cong, G., Jensen, C.S., Wu, D.: Spatial keyword query processing: an experimental evaluation. PVLDB (2013)Google Scholar
  10. 10.
    Cong, G., Jensen, C.S., Wu, D.: Efficient retrieval of the top-k most relevant spatial Web objects. PVLDB (2009)Google Scholar
  11. 11.
    De Felipe, I., Hristidis, V., Rishe, N.: Keyword search on spatial databases. In: ICDE (2008)Google Scholar
  12. 12.
    Deng, D., Li, G., Feng, J.: A pivotal prefix based filtering algorithm for string similarity search. In: SIGMOD, pp. 673–684. ACM (2014)Google Scholar
  13. 13.
    Faloutsos, C., Ranganathan, M., Manolopoulos, Y.: Fast subsequence matching in time-series databases, volume 23. ACM (1994)Google Scholar
  14. 14.
    Gravano, L., Ipeirotis, P.G., Jagadish, H.V., Koudas, N., Muthukrishnan, S., Srivastava, D., et al.: Approximate string joins in a database (almost) for free. In: VLDB (2001)Google Scholar
  15. 15.
    Guo, T., Cao, X., Cong, G.: Efficient algorithms for answering the m-closest keywords query. In: SIGMOD, pp. 405–418. ACM (2015)Google Scholar
  16. 16.
    Jeung, H., Yiu, M.L., Zhou, X., Jensen, C.S., Shen, H.T.: Discovery of convoys in trajectory databases. VLDBJ (2008)Google Scholar
  17. 17.
    Jiang, Y., Li, G., Feng, J., Li, W.-S.: String similarity joins: An experimental evaluation. PVLDB 7(8), 625–636 (2014)Google Scholar
  18. 18.
    Jiang, M., Fu, A.W.-C., Wong, R.C.-W.: Exact top-k nearest keyword search in large networks. In: SIGMOD, pp. 393–404. ACM (2015)Google Scholar
  19. 19.
    Karp, R.M.: Reducibility among combinatorial problems. Springer, New York (1972)CrossRefzbMATHGoogle Scholar
  20. 20.
    Li, C., Wang, B., Yang, X.: Vgram: Improving performance of approximate queries on string collections using variable-length grams. In: PVLDB, pp. 303–314. VLDB Endowment (2007)Google Scholar
  21. 21.
    Li, G., Feng, J., Xu, J.: Desks: Direction-aware spatial keyword search. In: ICDE, pp. 474–485. IEEE (2012)Google Scholar
  22. 22.
    Li, G., Deng, D., Feng, J.: A partition-based method for string similarity joins with edit-distance constraints. TODS (2013)Google Scholar
  23. 23.
    Li, Y., Yiu, M.L., Gong, Z., et al.: Discovering longest-lasting correlation in sequence databases. PVLDB 6(14), 1666–1677 (2013)Google Scholar
  24. 24.
    Lu, J., Lu, Y., Cong, G.: Reverse spatial and textual k nearest neighbor search. In: SIGMOD, pp. 349–360. ACM (2011)Google Scholar
  25. 25.
    Mueen, A., Hamooni, H., Estrada, T.: Time series join on subsequence correlation. In: ICDM, pp. 450–459. IEEE (2014)Google Scholar
  26. 26.
    Navarro, G.: A guided tour to approximate string matching. CSUR (2001)Google Scholar
  27. 27.
    Pfoser, D., Jensen, C.S., Theodoridis, Y., et al.: Novel approaches to the indexing of moving object trajectories. In: VLDB (2000)Google Scholar
  28. 28.
    Rocha-Junior, J.B., Nørvåg, K.: Top-k spatial keyword queries on road networks. In: EDBT, pp. 168–179. ACM (2012)Google Scholar
  29. 29.
    Sarawagi, S., Kirpal, A.: Efficient set joins on similarity predicates. In: SIGMOD (2004)Google Scholar
  30. 30.
    Shang, S., Ding, R., Zheng, K., Jensen, C.S., Kalnis, P., Zhou, X.: Personalized trajectory matching in spatial networks. VLDBJ 23(3), 449–468 (2014)CrossRefGoogle Scholar
  31. 31.
    Shang, S., Chen, L., Jensen, C.S., Wen, J.-R., Kalnis, P.: Searching trajectories by regions of interest. TKDE 29(7), 1549–1562 (2017)Google Scholar
  32. 32.
    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
  33. 33.
    Tao, Y., Papadias, D., Shen, Q.: Continuous nearest neighbor search. In: VLDB, pp. 287–298. VLDB Endowment (2002)Google Scholar
  34. 34.
    Vlachos, M., Kollios, G., Gunopulos, D.: Discovering similar multidimensional trajectories. In: ICDE (2002)Google Scholar
  35. 35.
    Wang, J., Li, G., Deng, D., Zhang, Y., Feng, J.: Two birds with one stone: An efficient hierarchical framework for top-k and threshold-based string similarity search. In: ICDE, pp. 519–530. IEEE (2015)Google Scholar
  36. 36.
    Wang, X., Ding, X., Tung, A.K., Zhang, Z.: Efficient and effective knn sequence search with approximate n-grams. PVLDB (2013)Google Scholar
  37. 37.
    Wu, D., Yiu, M.L., Jensen, C.S., Cong, G.: Efficient continuously moving top-k spatial keyword query processing. In: ICDE, pp. 541–552. IEEE (2011)Google Scholar
  38. 38.
    Yang, X., Wang, Y., Wang, B., Wang, W.: Local filtering: Improving the performance of approximate queries on string collections. In: SIGMOD, pp. 377–392. ACM (2015)Google Scholar
  39. 39.
    Yao, B., Li, F., Hadjieleftheriou, M., Hou, K.: Approximate string search in spatial databases. In: ICDE (2010)Google Scholar
  40. 40.
    Yi, B.-K., Jagadish, H., Faloutsos, C.: Efficient retrieval of similar time sequences under time warping. In: ICDE. IEEE (1998)Google Scholar
  41. 41.
    Zhang, D., Chee, Y.M., Mondal, A., Tung, A.K., Kitsuregawa, M.: Keyword search in spatial databases: Towards searching by document. In: ICDE, pp. 688–699. IEEE (2009)Google Scholar
  42. 42.
    Zhang, D., Ooi, B.C., Tung, A.K.: Locating mapped resources in Web 2.0. In: ICDE, pp. 521–532. IEEE (2010)Google Scholar
  43. 43.
    Zhang, D., Tan, K.-L., Tung, A.K.: Scalable top-k spatial keyword search. In: EDBT, pp. 359–370. ACM (2013)Google Scholar
  44. 44.
    Zheng, K., Trajcevski, G., Zhou, X., Scheuermann, P.: Probabilistic range queries for uncertain trajectories on road networks. In: EDBT (2011)Google Scholar
  45. 45.
    Zheng, K., Zheng, Y., Xie, X., Zhou, X.: Reducing uncertainty of low-sampling-rate trajectories. In: ICDE (2012)Google Scholar
  46. 46.
    Zheng, K., Shang, S., Yuan, N.J., Yang, Y.: Towards efficient search for activity trajectories. In: ICDE (2013)Google Scholar
  47. 47.
    Zheng, K., Zheng, Y., Yuan, N.J., Shang, S.: On discovery of gathering patterns from trajectories. In: ICDE (2013)Google Scholar
  48. 48.
    Zheng, B., Zheng, K., Sharaf, M., Zhou, X., Sadiq, S.: Efficient retrieval of top-k most similar users from travel smart card data. In: MDM (2014)Google Scholar
  49. 49.
    Zheng, B., Yuan, N.J., Zheng, K., Xie, X., Sadiq, S., Zhou, X.: Approximate keyword search in semantic trajectory database. In: ICDE, pp. 975–986. IEEE (2015)Google Scholar
  50. 50.
    Zhou, Y., Xie, X., Wang, C., Gong, Y., Ma, W.-Y.: Hybrid index structures for location-based Web search. In: CIKM, pp. 155–162. ACM (2005)Google Scholar

Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2018

Authors and Affiliations

  • Bolong Zheng
    • 1
  • Kai Zheng
    • 2
    Email author
  • Peter Scheuermann
    • 3
  • Xiaofang Zhou
    • 4
  • Quoc Viet Hung Nguyen
    • 5
  • Chenliang Li
    • 6
  1. 1.School of Data and Computer ScienceSun Yat-sen UniversityGuangzhou ShiChina
  2. 2.School of Computer Science and Engineering and Big Data Research CenterUniversity of Electronic Science and Technology of ChinaChengduChina
  3. 3.Northwestern UniversityEvanstonUSA
  4. 4.The University of QueenslandBrisbaneAustralia
  5. 5.Griffith UniversityNathanAustralia
  6. 6.State Key Laboratory of Software Engineering Computer SchoolWuhan UniversityWuhanChina

Personalised recommendations