Group Trip Planning Queries in Spatial Databases

  • Tanzima Hashem
  • Tahrima Hashem
  • Mohammed Eunus Ali
  • Lars Kulik
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8098)


Location-based social networks grow at a remarkable pace. Current location-aware mobile devices enable us to access these networks from anywhere and to connect to friends via social networks in a seamless manner. These networks allow people to interact with friends and colleagues in a novel way, for example, they may want to spontaneously meet in the next hour for dinner at a restaurant nearby followed by a joint visit to a movie theater. This motivates a new query type, which we call a group trip planning (GTP) query: the group has an interest to minimize the total travel distance for all members, and this distance is the sum of each user’s travel distance from each user’s start location to destination via the restaurant and the movie theater. Formally, for a set of user source-destination pairs in a group and different types of data points (e.g., a movie theater versus a restaurant), a GTP query returns for each type of data points those locations that minimize the total travel distance for the entire group. We develop efficient algorithms to answer GTP queries, which we show in extensive experiments.


Group nearest neighbor queries Group trip planning queries Location-based services Location-based social networks Spatial Databases 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
  2. 2.
  3. 3.
  4. 4.
    Papadias, D., Shen, Q., Tao, Y., Mouratidis, K.: Group nearest neighbor queries. In: ICDE, p. 301 (2004)Google Scholar
  5. 5.
    Papadias, D., Tao, Y., Mouratidis, K., Hui, C.K.: Aggregate nearest neighbor queries in spatial databases. TODS 30(2), 529–576 (2005)CrossRefGoogle Scholar
  6. 6.
    Deng, K., Sadiq, S.W., Zhou, X., Xu, H., Fung, G.P.C., Lu, Y.: On group nearest group query processing. IEEE TKDE 24(2), 295–308 (2012)Google Scholar
  7. 7.
    Li, Y., Li, F., Yi, K., Yao, B., Wang, M.: Flexible aggregate similarity search. In: SIGMOD, pp. 1009–1020 (2011)Google Scholar
  8. 8.
    Sharifzadeh, M., Kolahdouzan, M., Shahabi, C.: The optimal sequenced route query. The VLDB Journal 17(4), 765–787 (2008)CrossRefGoogle Scholar
  9. 9.
    Li, F., Cheng, D., Hadjieleftheriou, M., Kollios, G., Teng, S.-H.: On trip planning queries in spatial databases. In: Medeiros, C.B., Egenhofer, M., Bertino, E. (eds.) SSTD 2005. LNCS, vol. 3633, pp. 273–290. Springer, Heidelberg (2005)CrossRefGoogle Scholar
  10. 10.
    Guttman, A.: R-trees: a dynamic index structure for spatial searching. In: SIGMOD, pp. 47–57 (1984)Google Scholar
  11. 11.
    Beckmann, N., Kriegel, H.P., Schneider, R., Seeger, B.: The R*-tree: an efficient and robust access method for points and rectangles. SIGMOD Rec. 19(2), 322–331 (1990)CrossRefGoogle Scholar
  12. 12.
    Hjaltason, G.R., Samet, H.: Ranking in spatial databases. In: International Symposium on Advances in Spatial Databases, pp. 83–95 (1995)Google Scholar
  13. 13.
    Li, F., Yao, B., Kumar, P.: Group enclosing queries. IEEE TKDE 23(10), 1526–1540 (2011)Google Scholar
  14. 14.
    Chen, H., Ku, W.S., Sun, M.T., Zimmermann, R.: The multi-rule partial sequenced route query. In: GIS, pp. 10:1–10:10(2008)Google Scholar
  15. 15.
    Ohsawa, Y., Htoo, H., Sonehara, N., Sakauchi, M.: Sequenced route query in road network distance based on incremental euclidean restriction. In: Liddle, S.W., Schewe, K.-D., Tjoa, A.M., Zhou, X. (eds.) DEXA 2012, Part I. LNCS, vol. 7446, pp. 484–491. Springer, Heidelberg (2012)CrossRefGoogle Scholar
  16. 16.
    Malviya, N., Madden, S., Bhattacharya, A.: A continuous query system for dynamic route planning. In: ICDE, pp. 792–803 (2011)Google Scholar
  17. 17.
    Chen, Z., Shen, H.T., Zhou, X.: Discovering popular routes from trajectories. In: ICDE, pp. 900–911 (2011)Google Scholar
  18. 18.
    Geisberger, R., Kobitzsch, M., Sanders, P.: Route planning with flexible objective functions. In: ALENEX, pp. 124–137 (2010)Google Scholar
  19. 19.
    Chen, Z., Shen, H.T., Zhou, X., Zheng, Y., Xie, X.: Searching trajectories by locations: an efficiency study. In: SIGMOD, pp. 255–266 (2010)Google Scholar
  20. 20.
    Roussopoulos, N., Kelley, S., Vincent, F.: Nearest neighbor queries. In: SIGMOD, pp. 71–79 (1995)Google Scholar
  21. 21.
    Hashem, T., Kulik, L., Zhang, R.: Privacy preserving group nearest neighbor queries. In: EDBT, pp. 489–500 (2010)Google Scholar
  22. 22.
    Mokbel, M.F., Chow, C.Y., Aref, W.G.: The new casper: query processing for location services without compromising privacy. In: VLDB, pp. 763–774 (2006)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Tanzima Hashem
    • 1
  • Tahrima Hashem
    • 2
  • Mohammed Eunus Ali
    • 1
  • Lars Kulik
    • 3
  1. 1.Department of Computer Science and EngineeringBangladesh University of Engineering and TechnologyDhakaBangladesh
  2. 2.Department of Computer Science and EngineeringDhaka UniversityDhakaBangladesh
  3. 3.Department of Computing and Information SystemUniversity of MelbourneAustralia

Personalised recommendations