Skip to main content
SpringerLink
Log in
Book cover

International Conference on Web Information Systems Engineering

WISE 2017: Web Information Systems Engineering – WISE 2017 pp 299–314Cite as

Location-Based Top-k Term Querying over Sliding Window

Part of the Lecture Notes in Computer Science book series (LNISA,volume 10569)

Abstract

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.

Keywords

  • Top-k
  • Term
  • Location

This is a preview of subscription content, access via your institution.

References

  1. Agarwal, P.K., Cormode, G., Huang, Z., Phillips, J., Wei, Z., Yi, K.: Mergeable summaries. In: PODS (2012)

    Google Scholar 

  2. Bansal, N., Koudas, N.: BlogScope: a system for online analysis of high volume text streams. In: VLDB (2007)

    Google Scholar 

  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_59

    CrossRef  Google Scholar 

  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. Cormode, G., Muthukrishnan, S.: An improved data stream summary: the count-min sketch and its applications. J. Algorithms 55(1), 58–75 (2005)

    CrossRef  MathSciNet  Google Scholar 

  6. Cormode, G., Muthukrishnan, S.: What’s hot and what’s not: tracking most frequent items dynamically. TODS 30(1), 249–278 (2005)

    CrossRef  Google Scholar 

  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_33

    CrossRef  Google Scholar 

  8. Felipe, I.D., Hristidis, V., Rishe, N.: Keyword search on spatial databases. In: ICDE (2008)

    Google Scholar 

  9. Finkel, R.A., Bentley, J.L.: Quad trees a data structure for retrieval on composite keys. Acta Inform. 4(1), 1–9 (1974)

    CrossRef  Google Scholar 

  10. Li, F., Yao, B., Kumar, P.: Group enclosing queries. TKDE 23(10), 1526–1540 (2011)

    Google Scholar 

  11. Li, F., Yao, B., Tang, M., Hadjieleftheriou, M.: Spatial approximate string search. TKDE 25(6), 1394–1409 (2013)

    Google Scholar 

  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)

    CrossRef  Google Scholar 

  13. Li, Y., Li, F., Yi, K., Yao, B., Wang, M.: Flexible aggregate similarity search. In: SIGMOD (2011)

    Google Scholar 

  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. Lian, X., Chen, L.: Shooting top-k stars in uncertain databases. VLDBJ 20(6), 819–840 (2011)

    CrossRef  Google Scholar 

  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)

    CrossRef  Google Scholar 

  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_6

    CrossRef  Google Scholar 

  18. Manku, G.S., Motwani, R.: Approximate frequency counts over data streams. In VLDB (2002)

    CrossRef  Google Scholar 

  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_27

    CrossRef  Google Scholar 

  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)

    CrossRef  Google Scholar 

  21. Misra, J., Gries, D.: Finding repeated elements. Sci. Comput. Program. 2(2), 143–152 (1982)

    CrossRef  MathSciNet  Google Scholar 

  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_13

    CrossRef  Google Scholar 

  23. Nutanong, S., Tanin, E., Zhang, R.: Incremental evaluation of visible nearest neighbor queries. TKDE 22(5), 665–681 (2010)

    Google Scholar 

  24. Sankaranarayanan, J., Samet, H., Teitler, B.E., Lieberman, M.D., Sperling, J.: Twitterstand: news in tweets. In: GIS (2009)

    Google Scholar 

  25. Shang, S., Ding, R., Yuan, B., et al.: User oriented trajectory search for trip recommendation. In: EDBT (2012)

    Google Scholar 

  26. Shang, S., Ding, R., Zheng, K., et al.: Personalized trajectory matching in spatial networks. VLDBJ 23(3), 449–468 (2014)

    CrossRef  Google Scholar 

  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. Shang, S., Chen, L., Wei, Z., et al.: Collective travel planning in spatial networks. TKDE 28(5), 1132–1146 (2016)

    Google Scholar 

  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. Shang, S., Chen, L., Wei, Z., et al.: Trajectory similarity join in spatial networks. PVLDB 10(11), 1178–1189 (2017)

    Google Scholar 

  31. Skovsgaard, A., Sidlauskas, D., Jensen, C.S.: Scalable top-k spatio-temporal term querying. In: ICDE (2014)

    Google Scholar 

  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. 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. Xiao, X., Yao, B., Li, F.: Optimal location queries in road network databases. In: ICDE (2011)

    Google Scholar 

  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. 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. Yao, B., Li, F., Kumar, P.: Reverse furthest neighbors in spatial databases. In: ICDE (2009)

    Google Scholar 

  38. Yao, B., Li, F., Hadjieleftheriou, M., Hou, K.: Approximate string search in spatial databases. In: ICDE (2010)

    Google Scholar 

  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. Yao, B., Tang, M., Li, F.: Multi-approximate-keyword routing in GIS data. In: GIS (2011)

    Google Scholar 

  41. Yao, B., Li, F., Xiao, X.: Secure nearest neighbor revisited. In: ICDE (2013)

    Google Scholar 

  42. Yao, B., Xiao, X., Li, F., Wu, Y.: Dynamic monitoring of optimal locations in road network databases. VLDBJ 23(5), 697–720 (2014)

    CrossRef  Google Scholar 

  43. Zhang, C., Zhang, Y., Zhang, W., Lin, X.: Inverted linear quadtree: Efficient top k spatial keyword search. In: ICDE (2013)

    Google Scholar 

  44. Zhang, D., Chan, C., Tan, K.: Processing spatial keyword query as a top-k aggregation query. In: SIGIR (2014)

    Google Scholar 

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

    Google Scholar 

  46. Zhao, K., Chen, L., Cong, G.: Topic exploration in spatio-temporal document collections. In: SIGMOD (2016)

    Google Scholar 

Download references

Acknowledgement

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.

Author information

Authors and Affiliations

  1. Shanghai Jiao Tong University, Shanghai, China

    Ying Xu, Bin Yao & Fang Li

  2. Hong Kong Baptist University, Hong Kong, China

    Lisi Chen

  3. King Abdullah University of Science and Technology, Thuwal, Saudi Arabia

    Shuo Shang

  4. Xiamen University of Technology, Xiamen, China

    Shunzhi Zhu

  5. Soochow University, Soochow, China

    Kai Zheng

Authors
  1. Ying Xu
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Lisi Chen
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Bin Yao
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Shuo Shang
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Shunzhi Zhu
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Kai Zheng
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Fang Li
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding authors

Correspondence to Bin Yao or Shuo Shang .

Editor information

Editors and Affiliations

  1. University of Sydney, Darlington, NSW, Australia

    Athman Bouguettaya

  2. Zhejiang University, Hangzhou, China

    Prof. Yunjun Gao

  3. Institute of Computing for Physics and Technology, Protvino, Russia

    Andrey Klimenko

  4. Nanyang Technological University, Singapore, Singapore

    Lu Chen

  5. King Abdullah University of Science and Technology, Thuwal, Saudi Arabia

    Xiangliang Zhang

  6. Institute of Computing for Physics and Technology, Protvino, Russia

    Fedor Dzerzhinskiy

  7. Shanghai Jiao Tong University, Minhang Qu, China

    Weijia Jia

  8. Institute of Computing for Physics and Technology, Protvino, Russia

    Stanislav V. Klimenko

  9. City University of Hong Kong, Kowloon, Hong Kong

    Qing Li

Rights and permissions

Reprints and Permissions

Copyright information

© 2017 Springer International Publishing AG

About this paper

Cite this paper

Xu, Y. et al. (2017). Location-Based Top-k Term Querying over Sliding Window. In: Bouguettaya, A., et al. Web Information Systems Engineering – WISE 2017. WISE 2017. Lecture Notes in Computer Science(), vol 10569. Springer, Cham. https://doi.org/10.1007/978-3-319-68783-4_21

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-68783-4_21

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-68782-7

  • Online ISBN: 978-3-319-68783-4

  • eBook Packages: Computer ScienceComputer Science (R0)

search

Navigation