Location-Based Top-k Term Querying over Sliding Window

  • Ying Xu
  • Lisi Chen
  • Bin YaoEmail author
  • Shuo ShangEmail author
  • Shunzhi Zhu
  • Kai Zheng
  • Fang Li
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10569)


In part due to the proliferation of GPS-equipped mobile devices, massive svolumes of geo-tagged streaming text messages are becoming available on social media. It is of great interest to discover most frequent nearby terms from such tremendous stream data. In this paper, we present novel indexing, updating, and query processing techniques that are capable of discovering top-k locally popular nearby terms over a sliding window. Specifically, given a query location and a set of geo-tagged messages within a sliding window, we study the problem of searching for the top-k terms by considering both the term frequency and the proximities between the messages containing the term and the query location. We develop a novel and efficient mechanism to solve the problem, including a quad-tree based indexing structure, indexing update technique, and a best-first based searching algorithm. An empirical study is conducted to show that our proposed techniques are efficient and fit for users’ requirements through varying a number of parameters.


Top-k Term Location 



This work was supported by the NSFC (U1636210, 61373156, 91438121 and 61672351), the National Basic Research Program (973 Program, No. 2015CB352403), the National Key Research and Development Program of China (2016YFB0700502), the Scientific Innovation Act of STCSM (15JC1402400) and the Microsoft Research Asia.


  1. 1.
    Agarwal, P.K., Cormode, G., Huang, Z., Phillips, J., Wei, Z., Yi, K.: Mergeable summaries. In: PODS (2012)Google Scholar
  2. 2.
    Bansal, N., Koudas, N.: BlogScope: a system for online analysis of high volume text streams. In: VLDB (2007)Google Scholar
  3. 3.
    Charikar, M., Chen, K., Farach-Colton, M.: Finding frequent items in data streams. In: Widmayer, P., Eidenbenz, S., Triguero, F., Morales, R., Conejo, R., Hennessy, M. (eds.) ICALP 2002. LNCS, vol. 2380, pp. 693–703. Springer, Heidelberg (2002). doi: 10.1007/3-540-45465-9_59CrossRefGoogle Scholar
  4. 4.
    Cong, G., Jensen, C.S., Wu, D.: Efficient retrieval of the top-k most relevant spatial web objects. PVLDB 2(1), 337–348 (2009)Google Scholar
  5. 5.
    Cormode, G., Muthukrishnan, S.: An improved data stream summary: the count-min sketch and its applications. J. Algorithms 55(1), 58–75 (2005)MathSciNetCrossRefGoogle Scholar
  6. 6.
    Cormode, G., Muthukrishnan, S.: What’s hot and what’s not: tracking most frequent items dynamically. TODS 30(1), 249–278 (2005)CrossRefGoogle Scholar
  7. 7.
    Demaine, E.D., López-Ortiz, A., Munro, J.I.: Frequency estimation of internet packet streams with limited space. In: Möhring, R., Raman, R. (eds.) ESA 2002. LNCS, vol. 2461, pp. 348–360. Springer, Heidelberg (2002). doi: 10.1007/3-540-45749-6_33CrossRefGoogle Scholar
  8. 8.
    Felipe, I.D., Hristidis, V., Rishe, N.: Keyword search on spatial databases. In: ICDE (2008)Google Scholar
  9. 9.
    Finkel, R.A., Bentley, J.L.: Quad trees a data structure for retrieval on composite keys. Acta Inform. 4(1), 1–9 (1974)CrossRefGoogle Scholar
  10. 10.
    Li, F., Yao, B., Kumar, P.: Group enclosing queries. TKDE 23(10), 1526–1540 (2011)Google Scholar
  11. 11.
    Li, F., Yao, B., Tang, M., Hadjieleftheriou, M.: Spatial approximate string search. TKDE 25(6), 1394–1409 (2013)Google Scholar
  12. 12.
    Li, F., Yi, K., Tao, Y., Yao, B., Li, Y., Xie, D., Wang, M.: Exact and approximate flexible aggregate similarity search. VLDBJ 25(3), 317–338 (2016)CrossRefGoogle Scholar
  13. 13.
    Li, Y., Li, F., Yi, K., Yao, B., Wang, M.: Flexible aggregate similarity search. In: SIGMOD (2011)Google Scholar
  14. 14.
    Li, Z., Lee, K.C.K., Zheng, B., Lee, W., Lee, D.L., Wang, X.: IR-Tree: an efficient index for geographic document search. TKDE 23(4), 585–599 (2011)Google Scholar
  15. 15.
    Lian, X., Chen, L.: Shooting top-k stars in uncertain databases. VLDBJ 20(6), 819–840 (2011)CrossRefGoogle Scholar
  16. 16.
    Karp, R.M., Shenker, S., Papadimitriou, C.H.: A simple algorithm for finding frequent elements in streams and bags. TODS 28(1), 51–55 (2003)CrossRefGoogle Scholar
  17. 17.
    Ozsoy, M.G., Onal, K.D., Altingovde, I.S.: Result diversification for tweet search. In: Benatallah, B., Bestavros, A., Manolopoulos, Y., Vakali, A., Zhang, Y. (eds.) WISE 2014. LNCS, vol. 8787, pp. 78–89. Springer, Cham (2014). doi: 10.1007/978-3-319-11746-1_6CrossRefGoogle Scholar
  18. 18.
    Manku, G.S., Motwani, R.: Approximate frequency counts over data streams. In VLDB (2002)CrossRefGoogle Scholar
  19. 19.
    Metwally, A., Agrawal, D., El Abbadi, A.: Efficient computation of frequent and top-k elements in data streams. In: Eiter, T., Libkin, L. (eds.) ICDT 2005. LNCS, vol. 3363, pp. 398–412. Springer, Heidelberg (2004). doi: 10.1007/978-3-540-30570-5_27CrossRefGoogle Scholar
  20. 20.
    Metwally, A., Agrawal, D., El Abbadi, A.: An integrated efficient solution for computing frequent and top-k elements in data streams. TODS 31(3), 1095–1133 (2006)CrossRefGoogle Scholar
  21. 21.
    Misra, J., Gries, D.: Finding repeated elements. Sci. Comput. Program. 2(2), 143–152 (1982)MathSciNetCrossRefGoogle Scholar
  22. 22.
    Rocha-Junior, J.B., Gkorgkas, O., Jonassen, S., Nørvåg, K.: Efficient processing of top-k spatial keyword queries. In: Pfoser, D., Tao, Y., Mouratidis, K., Nascimento, M.A., Mokbel, M., Shekhar, S., Huang, Y. (eds.) SSTD 2011. LNCS, vol. 6849, pp. 205–222. Springer, Heidelberg (2011). doi: 10.1007/978-3-642-22922-0_13CrossRefGoogle Scholar
  23. 23.
    Nutanong, S., Tanin, E., Zhang, R.: Incremental evaluation of visible nearest neighbor queries. TKDE 22(5), 665–681 (2010)Google Scholar
  24. 24.
    Sankaranarayanan, J., Samet, H., Teitler, B.E., Lieberman, M.D., Sperling, J.: Twitterstand: news in tweets. In: GIS (2009)Google Scholar
  25. 25.
    Shang, S., Ding, R., Yuan, B., et al.: User oriented trajectory search for trip recommendation. In: EDBT (2012)Google Scholar
  26. 26.
    Shang, S., Ding, R., Zheng, K., et al.: Personalized trajectory matching in spatial networks. VLDBJ 23(3), 449–468 (2014)CrossRefGoogle Scholar
  27. 27.
    Shang, S., Zheng, K., Jensen, C.S., et al.: Discovery of path nearby clusters in spatial networks. TKDE 27(6), 1505–1518 (2015)Google Scholar
  28. 28.
    Shang, S., Chen, L., Wei, Z., et al.: Collective travel planning in spatial networks. TKDE 28(5), 1132–1146 (2016)Google Scholar
  29. 29.
    Shang, S., Chen, L., Jensen, C.S., et al.: Searching trajectories by regions of interest. TKDE 29(7), 1549–1562 (2017)Google Scholar
  30. 30.
    Shang, S., Chen, L., Wei, Z., et al.: Trajectory similarity join in spatial networks. PVLDB 10(11), 1178–1189 (2017)Google Scholar
  31. 31.
    Skovsgaard, A., Sidlauskas, D., Jensen, C.S.: Scalable top-k spatio-temporal term querying. In: ICDE (2014)Google Scholar
  32. 32.
    Teitler, B.E., Lieberman, M.D., Panozzo, D., Sankaranarayanan, J., Samet, H., Sperling, J.: Newsstand: a new view on news. In: GIS (2008)Google Scholar
  33. 33.
    Wang, Z., Wang, D., Yao, B., Guo, M.: Probabilistic range query over uncertain moving objects in constrained two-dimensional space. TKDE 27(3), 866–879 (2015)Google Scholar
  34. 34.
    Xiao, X., Yao, B., Li, F.: Optimal location queries in road network databases. In: ICDE (2011)Google Scholar
  35. 35.
    Xie, D., Li, F., Yao, B., Li, G., Zhou, L., Guo, M.: Simba: efficient in-memory spatial analytics. In: SIGMOD (2016)Google Scholar
  36. 36.
    Xie, D., Li, G., Yao, B., Wei, X., Xiao, X., Gao, Y., Guo, M.: Practical private shortest path computation based on oblivious storage. In: ICDE (2016)Google Scholar
  37. 37.
    Yao, B., Li, F., Kumar, P.: Reverse furthest neighbors in spatial databases. In: ICDE (2009)Google Scholar
  38. 38.
    Yao, B., Li, F., Hadjieleftheriou, M., Hou, K.: Approximate string search in spatial databases. In: ICDE (2010)Google Scholar
  39. 39.
    Yao, B., Li, F., Kumar, P.: K nearest neighbor queries and KNN-joins in large relational databases (almost) for free. In: ICDE (2010)Google Scholar
  40. 40.
    Yao, B., Tang, M., Li, F.: Multi-approximate-keyword routing in GIS data. In: GIS (2011)Google Scholar
  41. 41.
    Yao, B., Li, F., Xiao, X.: Secure nearest neighbor revisited. In: ICDE (2013)Google Scholar
  42. 42.
    Yao, B., Xiao, X., Li, F., Wu, Y.: Dynamic monitoring of optimal locations in road network databases. VLDBJ 23(5), 697–720 (2014)CrossRefGoogle Scholar
  43. 43.
    Zhang, C., Zhang, Y., Zhang, W., Lin, X.: Inverted linear quadtree: Efficient top k spatial keyword search. In: ICDE (2013)Google Scholar
  44. 44.
    Zhang, D., Chan, C., Tan, K.: Processing spatial keyword query as a top-k aggregation query. In: SIGIR (2014)Google Scholar
  45. 45.
    Zhang, D., Tan, K., Tung, A.K.H.: Scalable top-k spatial keyword search. In: EDBT, pp. 359–370 (2013)Google Scholar
  46. 46.
    Zhao, K., Chen, L., Cong, G.: Topic exploration in spatio-temporal document collections. In: SIGMOD (2016)Google Scholar

Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  1. 1.Shanghai Jiao Tong UniversityShanghaiChina
  2. 2.Hong Kong Baptist UniversityHong KongChina
  3. 3.King Abdullah University of Science and TechnologyThuwalSaudi Arabia
  4. 4.Xiamen University of TechnologyXiamenChina
  5. 5.Soochow UniversitySoochowChina

Personalised recommendations