Advertisement

Linear Path Skyline Computation in Bicriteria Networks

  • Michael Shekelyan
  • Gregor Jossé
  • Matthias Schubert
  • Hans-Peter Kriegel
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8421)

Abstract

A bicriteria network is an interlinked data set where edges are labeled with two cost attributes. An example is a road network where edges represent road segments being labeled with traversal time and energy consumption. To measure the proximity of two nodes in network data, the common method is to compute a cost optimal path between the nodes. In a bicriteria network, there often is no unique path being optimal w.r.t. both types of cost. Instead, a path skyline describes the set of non-dominated paths that are optimal under varying preference functions. In this paper, we examine the subset of the path skyline which is optimal under the most common type of preference function, the weighted sum. We will examine characteristics of this more strict domination relation. Furthermore, we introduce techniques to efficiently maintain the set of linearly non-dominated paths. Finally, we will introduce a new algorithm to compute all linearly non-dominated paths denoted as linear path skyline. In our experimental evaluation, we will compare our new approach to other methods for computing the linear skyline and efficient approaches to compute path skylines.

Keywords

Road Network Short Path Problem Skyline Query Cost Criterion Cost Vector 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Borzsonyi, S., Kossmann, D., Stocker, K.: The skyline operator. In: Proceedings of the 17th International Conference on Data Engineering (ICDE), Heidelberg, Germany (2001)Google Scholar
  2. 2.
    Kriegel, H.P., Renz, M., Schubert, M.: Route skyline queries: a multi-preference path planning approach. In: Proceedings of the 26th International Conference on Data Engineering (ICDE), Long Beach, CA, pp. 261–272 (2010)Google Scholar
  3. 3.
    Ehrgott, M.: Multicriteria optimization. Springer (2005)Google Scholar
  4. 4.
    Tan, K.L., Eng, P.K., Ooi, B.C.: Efficient progressive skyline computation. In: Proceedings of the 27th International Conference on Very Large Data Bases (VLDB), Roma, Italy (2001)Google Scholar
  5. 5.
    Kossmann, D., Ramsak, F., Rost, S.: Shooting stars in the sky: an online algorithm for skyline queries. In: Proceedings of the 28th International Conference on Very Large Data Bases (VLDB), Hong Kong, China (2002)Google Scholar
  6. 6.
    Papadias, D., Tao, Y., Fu, G., Seeger, B.: An optimal and progressive algorithm for skyline queries. In: Proceedings of the ACM International Conference on Management of Data (SIGMOD), San Diego, CA (2003)Google Scholar
  7. 7.
    Chan, C.-Y., Jagadish, H.V., Tan, K.-L., Tung, A.K.H., Zhang, Z.: On high dimensional skylines. In: Ioannidis, Y., Scholl, M.H., Schmidt, J.W., Matthes, F., Hatzopoulos, M., Böhm, K., Kemper, A., Grust, T., Böhm, C. (eds.) EDBT 2006. LNCS, vol. 3896, pp. 478–495. Springer, Heidelberg (2006)CrossRefGoogle Scholar
  8. 8.
    Zhang, Z., Guo, X., H. L., Tung, A.K.H., Wang, N.: Discovering strong skyline points in high dimensional spaces. In: CIKM 2005: Proceedings of the 14th ACM International Conference on Information and Knowledge Management, Bremen, Germany (2005)Google Scholar
  9. 9.
    Lin, X., Yuan, Y., Zhang, Q., Zhang, Y.: Selecting stars: the k most representitive skyline operator. In: Proceedings of the 23th International Conference on Data Engineering (ICDE), Istanbul, Turkey (2007)Google Scholar
  10. 10.
    Pei, J., Jin, W., Ester, M., Tao, Y.: Catching the best views of skyline: a semantic approach based on decisive subspaces. In: VLDB 2005: Proceedings of the 31st International Conference on Very Large Data Bases,Trondheim, Norway (2005)Google Scholar
  11. 11.
    Deng, K., Zhou, Y., Shen, H.T.: Multi-source query processing in road networks. In: Proceedings of the 23th International Conference on Data Engineering (ICDE), Istanbul, Turkey (2007)Google Scholar
  12. 12.
    Huang, X., Jensen, C.S.: In-route skyline querying for location-based services. In: Proc. of the Int. Workshop on Web and Wireless Geographical Information Systems (W2GIS), Goyang, Korea, pp. 120–135 (2004)Google Scholar
  13. 13.
    Jang, S.M., Yoo, J.S.: Processing continuous skyline queries in road networks. In: International Symposium on Computer Science and its Applications, CSA 2008 (2008)Google Scholar
  14. 14.
    Hansen, P.: Bicriterion path problems. In: Multiple Criteria Decision Making Theory and Application, pp. 109–127. Springer (1980)Google Scholar
  15. 15.
    Raith, A., Ehrgott, M.: A comparison of solution strategies for biobjective shortest path problems. Computers & Operations Research 36(4), 1299–1331 (2009)CrossRefzbMATHMathSciNetGoogle Scholar
  16. 16.
    Brumbaugh-Smith, J., Shier, D.: An empirical investigation of some bicriterion shortest path algorithms. European Journal of Operational Research 43(2), 216–224 (1989)CrossRefzbMATHMathSciNetGoogle Scholar
  17. 17.
    Matthew Carlyle, W., Kevin Wood, R.: Near-shortest and k-shortest simple paths. Networks 46(2), 98–109 (2005)CrossRefzbMATHMathSciNetGoogle Scholar
  18. 18.
    Mote, J., Murthy, I., Olson, D.L.: A parametric approach to solving bicriterion shortest path problems. European Journal of Operational Research 53(1), 81–92 (1991)CrossRefzbMATHGoogle Scholar
  19. 19.
    Delling, D., Sanders, P., Schultes, D., Wagner, D.: Engineering route planning algorithms. In: Lerner, J., Wagner, D., Zweig, K.A. (eds.) Algorithmics of Large and Complex Networks. LNCS, vol. 5515, pp. 117–139. Springer, Heidelberg (2009)CrossRefGoogle Scholar
  20. 20.
    Machuca, E., Mandow, L.: Multiobjective route planning with precalculated heuristics. In: Proc. of the 15th Portuguese Conference on Artificial Intelligence (EPIA 2011), pp. 98–107(2011)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Michael Shekelyan
    • 1
  • Gregor Jossé
    • 1
  • Matthias Schubert
    • 1
  • Hans-Peter Kriegel
    • 1
  1. 1.Institute for InformaticsLudwig-Maximilians-Universität MünchenMunichGermany

Personalised recommendations