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Modified MinG Algorithm to Find Top-K Shortest Paths from large RDF Graphs

  • Zohaib Hassan
  • Mohammad Abdul Qadir
  • Muhammad Arshad Islam
  • Umer Shahzad
  • Nadeem Akhter
Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 641)

Abstract

MinG algorithm indexes large RDF graphs in an efficient way and then uses the index to answer all path queries between two nodes of the graph. MinG reduces the computational and space complexity of indexing by not creating a special type of adjacency matrix called Path Type Matrix at each level of indexing. We only need Path Type Matrices at first and last level of indexing. MinG was modified to answer top-K shortest paths. The experiments were performed on specific case studies. Gain in the performance is significant due to reduction in the space to index a graph and also reduction in computation time to answer path queries.

Keywords

Top-K Shortest Paths Graph Traversal Graph Indexing Algorithm Graph Mining Semantic Web 

Reference

  1. 1.
    Wong, P.C., Haglin, D., Gillen, D., Chavarria, D., Castellana, V., Joslyn, C., Chappell, A., Zhang, S.: “A visual analytics paradigm enabling trillion-edge graph exploration”, n IEEE 5th Symposium on Large Data Analysis and Visualization (LDAV 2015), October 25-26, pp. 57–64. Illinois, Chicago (2015)Google Scholar
  2. 2.
    Tarjan, R.E.: Fast algorithms for solving path problems. J. ACM 28(3), 594–614 (1981)MathSciNetCrossRefzbMATHGoogle Scholar
  3. 3.
    Wang, J., Ntarmos, N., and Triantafillou, P. (2016) Indexing Query Graphs to Speed Up Graph Query Processing. In: EDBT: 19th International Conference on Extending Database Technology, Bordeaux, France, 15-18 March 2016Google Scholar
  4. 4.
    Eppstein, D.: Finding the k shortest paths. SIAM J. Computing 28(2), 652–673 (1998)MathSciNetCrossRefzbMATHGoogle Scholar
  5. 5.
    S. Barton, “Indexing graph structured data,” PhD Thesis, Masaryk University, Brno, Czech Republic, 2007Google Scholar
  6. 6.
    S. Barton, and P. Zezula, “Indexing structure for graph structured data,” in Mining Complex Data, Springer Berlin / Heidelberg, 2009, pp. 167-188. Studies in Computational Intelligence, Vol. 165. ISBN 978-3-540-88066-Google Scholar
  7. 7.
    Tarjan, R.E.: A unified approach to path problems. J. ACM 28(3), 577–593 (1981)MathSciNetCrossRefzbMATHGoogle Scholar
  8. 8.
    Matono, A., Amagasa, T., Yoshikawa, M., Uemura, S.: An indexing scheme for RDF and RDF Schema based on suffix arrays. Proceedings of SWDB’03, p. 2003. Co-located with VLDB, The first International Workshop on Semantic Web and Databases (2003)Google Scholar
  9. 9.
    S. Barton, “Indexing structure for discovering relationships in RDF graph recursively applying tree transformation”, in Proceedings of the Semantic Web Workshop at 27th Annual International ACM SIGIR Conference, pp. 58–68, 2004Google Scholar
  10. 10.
    Agrawal, R., Dar, S., Jagadish, H.V.: Direct transitive closure algorithms: design and performance evaluation. ACM Transactions on Database Systems 15(3), 427–458 (1990)MathSciNetCrossRefGoogle Scholar
  11. 11.
    P.F. Dietz. “Maintaining order in a linked list,” in STOC’82: Proceedings of the fourteenth annual ACM symposium on Theory of computing, pp. 122–127, New York, USA, 1982. ACM PressGoogle Scholar
  12. 12.
    E. Cohen, E. Halperin, H. Kaplan, and U. Zwick. “Reachability and distance queries via 2-hop labels,” in Proceedings of the 13th annual ACM-SIAM Symposium on Discrete algorithms, pp. 937–946, 2002Google Scholar
  13. 13.
    Schenkel, Ralf, Theobald, Anja, Weikum, Gerhard: HOPI: An Efficient Connection Index for Complex XML Document Collections. In: Bertino, Elisa, Christodoulakis, Stavros, Plexousakis, Dimitris, Christophides, Vassilis, Koubarakis, Manolis, Böhm, Klemens (eds.) EDBT 2004. LNCS, vol. 2992, pp. 237–255. Springer, Heidelberg (2004)CrossRefGoogle Scholar
  14. 14.
    R. Schenkel, A. Theobald, and G.Weikum. “Efficient creation and incremental maintenance of the HOPI index for complex xml document collections,” In ICDE, 2005Google Scholar
  15. 15.
    H. He, H. Wang, J. Yang, and P.S. Yu, “Compact reachability labeling for graph-structured data,” in CIKM ’05: Proceedings of the 14th ACM international conference on Information and knowledge management, pp. 594–601, New York, USA, 2005. ACM Press.Google Scholar
  16. 16.
    H. Wang, H. He, J. Yang, P.S. Yu, and J.X. Yu, “Dual Labeling: Answering Graph Reachability Queries in Constant Time,” in Proceedings of the 22 nd International Conference on Data Engineering (ICDE), pp. 75, 2006. IEEE Computer SocietyGoogle Scholar
  17. 17.
    S. Barton and P. Zezula, “rho Index – designing and evaluating an indexing structure for graph structured data.” Technical Report FIMU-RS-2006-07, Faculty of Informatics, Masaryk University, 2006.Google Scholar
  18. 18.
    Skiena, S.: Implementing Discrete Mathematics: Combinatorics and Graph Theory with Mathematica. Addison-Wesley, Reading, MA (1990)zbMATHGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  1. 1.Department of Computer ScienceCapital University of Science and TechnologyIslamabadPakistan

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