Advertisement

A Measure and Conquer Algorithm for the Minimum User Spatial-Aware Interest Group Query Problem

  • Chih-Yang HuangEmail author
  • Po-Chuan Chien
  • Yen Hung Chen
Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 1013)

Abstract

Location-based social networks are important issues in the recent decade. In modern social networks, websites such as Twitter, Facebook, and Plurk, attempt to get the accurate address positions from their users, and try to reduce the gap between virtuality and reality. This paper mainly aims at both the interests of Internet users and their real positions. This issue is called the spatial-aware interest group query problem (SIGQP). Given a user set U with n users, a keywords set W with m words, and a spatial objects set S with s items, each of which contains one or multiple keywords. If a user checks in a certain spatial object, it means the user could be interested in that part of keywords, which is countable to clarify the interests of the user. The SIGQP then tries to find a k-user set \(U_{k}\), \(k \le n\), such that the union of keywords of these k users will equal to W, and additionally, the diameter (longest Euclidean distance of two arbitrary users in \(U_k\)) should be as small as possible. The SIGQP has been proved as NP-Complete, and two heuristic algorithms have been proposed. Extended from SIGQP, the main problem of this paper prioritizes in finding the smallest k for \(U_{k}\) to cover all the keywords, with the users’ distance as the secondary criterion, called as “minimum user spatial-aware interest group query problem” (MUSIGQP). This paper further designs an exact algorithm on a measure-&-conquer-based method to precisely solve this problem, and a performance analysis is given.

Keywords

Spatial-aware interest group query problem NP-Complete Exact algorithms Computing problem Group queries Location-based service 

Notes

Acknowledgment

This research was supported by the Ministry of Science and Technology in Taiwan under the grants MOST 107-2813-C-845-025-E.

References

  1. 1.
    Chen, L., Cong, G., Jensen, C.S., Wu, D.: Spatial keyword query processing: an experimental evaluation. In: Proceedings of the VLDB Endowment, vol. 6, pp. 217–228. VLDB Endowment (2013)Google Scholar
  2. 2.
    Chen, S.J., Lin, L.: Modeling team member characteristics for the formation of a multifunctional team in concurrent engineering. IEEE Trans. Eng. Manag. 51(2), 111–124 (2004)MathSciNetCrossRefGoogle Scholar
  3. 3.
    Cong, G., Jensen, C.S., Wu, D.: Efficient retrieval of the top-k most relevant spatial web objects. Proc. VLDB Endow. 2(1), 337–348 (2009)CrossRefGoogle Scholar
  4. 4.
    Corral, A., Manolopoulos, Y., Theodoridis, Y., Vassilakopoulos, M.: Closest pair queries in spatial databases. In: ACM SIGMOD Record, vol. 29, pp. 189–200. ACM (2000)Google Scholar
  5. 5.
    De Felipe, I., Hristidis, V., Rishe, N.: Keyword search on spatial databases. In: IEEE 24th International Conference on Data Engineering, ICDE 2008, pp. 656–665. IEEE (2008)Google Scholar
  6. 6.
    Feige, U.: A threshold of ln n for approximating set cover. J. ACM (JACM) 45(4), 634–652 (1998)CrossRefGoogle Scholar
  7. 7.
    Fomin, F.V., Grandoni, F., Kratsch, D.: A measure & conquer approach for the analysis of exact algorithms. J. ACM (JACM) 56(5), 25 (2009)MathSciNetCrossRefGoogle Scholar
  8. 8.
    Garey, M.R., Johnson, D.S.: Computers and y: A Guide to the Theory of NP-Completeness (Series of Books in the Mathematical Sciences). Computers and Intractability, vol. 340 (1979)Google Scholar
  9. 9.
    Garg, N., Konjevod, G., Ravi, R.: A polylogarithmic approximation algorithm for the group steiner tree problem. J. Algorithms 37(1), 66–84 (2000)MathSciNetCrossRefGoogle Scholar
  10. 10.
    Hjaltason, G.R., Samet, H.: Incremental distance join algorithms for spatial databases. In: ACM SIGMOD Record, vol. 27, pp. 237–248. ACM (1998)Google Scholar
  11. 11.
    Hjaltason, G.R., Samet, H.: Distance browsing in spatial databases. ACM Trans. Database Syst. (TODS) 24(2), 265–318 (1999)CrossRefGoogle Scholar
  12. 12.
    Hochbaum, D.S.: Approximating covering and packing problems: set cover, vertex cover, independent set, and related problems. Approx. Algorithms NP-Hard Probl. 94–143 (1997)Google Scholar
  13. 13.
    Karpiński, M., Karpinski, M., Rytter, W.: Fast Parallel Algorithms for Graph Matching Problems, vol. 9. Oxford University Press, Oxford (1998)zbMATHGoogle Scholar
  14. 14.
    Katayama, N., Satoh, S.: The SR-tree: an index structure for high-dimensional nearest neighbor queries. In: ACM Sigmod Record, vol. 26, no. 2, pp. 369–380 (1997)CrossRefGoogle Scholar
  15. 15.
    Kolahdouzan, M., Shahabi, C.: Voronoi-based k nearest neighbor search for spatial network databases. In: Proceedings of the Thirtieth International Conference on Very Large Data Bases, vol. 30, pp. 840–851. VLDB Endowment (2004)Google Scholar
  16. 16.
    Lappas, T., Liu, K., Terzi, E.: Finding a team of experts in social networks. In: Proceedings of the 15th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 467–476. ACM (2009)Google Scholar
  17. 17.
    Li, C.T., Shan, M.K.: Team formation for generalized tasks in expertise social networks. In: 2010 IEEE Second International Conference on Social Computing (SocialCom), pp. 9–16. IEEE (2010)Google Scholar
  18. 18.
    Li, Y., Wu, D., Xu, J., Choi, B., Su, W.: Spatial-aware interest group queries in location-based social networks. Data Knowl. Eng. 92, 20–38 (2014)CrossRefGoogle Scholar
  19. 19.
    Liu, W., Sun, W., Chen, C., Huang, Y., Jing, Y., Chen, K.: Circle of friend query in geo-social networks. In: Lee, S., Peng, Z., Zhou, X., Moon, Y.-S., Unland, R., Yoo, J. (eds.) DASFAA 2012. LNCS, vol. 7239, pp. 126–137. Springer, Heidelberg (2012).  https://doi.org/10.1007/978-3-642-29035-0_9CrossRefGoogle Scholar
  20. 20.
    Long, C., Wong, R.C.W., Wang, K., Fu, A.W.C.: Collective spatial keyword queries: a distance owner-driven approach. In: Proceedings of the 2013 ACM SIGMOD International Conference on Management of Data, pp. 689–700. ACM (2013)Google Scholar
  21. 21.
    Martins, B., Silva, M.J., Andrade, L.: Indexing and ranking in Geo-IR systems. In: Proceedings of the 2005 Workshop on Geographic Information Retrieval, pp. 31–34. ACM (2005)Google Scholar
  22. 22.
    Nemhauser, G.L., Wolsey, L.A., Fisher, M.L.: An analysis of approximations for maximizing submodular set functions-I. Math. Program. 14(1), 265–294 (1978)MathSciNetCrossRefGoogle Scholar
  23. 23.
    Pagel, B.U., Six, H.W., Toben, H., Widmayer, P.: Towards an analysis of range query performance in spatial data structures. In: Proceedings of the Twelfth ACM SIGACT-SIGMOD-SIGART Symposium on Principles of Database Systems, pp. 214–221. ACM (1993)Google Scholar
  24. 24.
    Papadias, D., Shen, Q., Tao, Y., Mouratidis, K.: Group nearest neighbor queries. In: Proceedings of the 20th International Conference on Data Engineering, pp. 301–312. IEEE (2004)Google Scholar
  25. 25.
    Rocha-Junior, J.B., Gkorgkas, O., Jonassen, S., Nørvåg, K.: Efficient processing of top-k spatial keyword queries. In: Pfoser, D., et al. (eds.) SSTD 2011. LNCS, vol. 6849, pp. 205–222. Springer, Heidelberg (2011).  https://doi.org/10.1007/978-3-642-22922-0_13CrossRefGoogle Scholar
  26. 26.
    Ross, P.E.: Top 11 technologies of the decade. IEEE Spectrum 48(1), 27–63 (2011)CrossRefGoogle Scholar
  27. 27.
    Roussopoulos, N., Kelley, S., Vincent, F.: Nearest neighbor queries. In: ACM Sigmod Record, vol. 24, pp. 71–79. ACM (1995)CrossRefGoogle Scholar
  28. 28.
    Shin, H., Moon, B., Lee, S.: Adaptive multi-stage distance join processing. In: ACM SIGMOD Record, vol. 29, pp. 343–354. ACM (2000)Google Scholar
  29. 29.
    Tao, Y., Xiao, X., Cheng, R.: Range search on multidimensional uncertain data. ACM Trans. Database Syst. (TODS) 32(3), 15 (2007)CrossRefGoogle Scholar
  30. 30.
    Wu, D., Cong, G., Jensen, C.S.: A framework for efficient spatial web object retrieval. VLDB J. 21(6), 797–822 (2012)CrossRefGoogle Scholar
  31. 31.
    Wu, D., Yiu, M.L., Jensen, C.S., Cong, G.: Efficient continuously moving top-k spatial keyword query processing. In: 2011 IEEE 27th International Conference on Data Engineering (ICDE), pp. 541–552. IEEE (2011)Google Scholar
  32. 32.
    Yang, D.N., Shen, C.Y., Lee, W.C., Chen, M.S.: On socio-spatial group query for location-based social networks. In: Proceedings of the 18th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 949–957. ACM (2012)Google Scholar
  33. 33.
    Zhou, Y., Xie, X., Wang, C., Gong, Y., Ma, W.Y.: Hybrid index structures for location-based web search. In: Proceedings of the 14th ACM International Conference on Information and Knowledge Management, pp. 155–162. ACM (2005)Google Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2019

Authors and Affiliations

  1. 1.Department of Computer ScienceUniversity of TaipeiTaipeiTaiwan (R.O.C.)
  2. 2.Research Center for Information Technology InnovationAcademia SinicaTaipeiTaiwan (R.O.C.)

Personalised recommendations