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Indexing Structure for Graph-Structured Data

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Mining Complex Data

Part of the book series: Studies in Computational Intelligence ((SCI,volume 165))

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Abstract

An own design of an indexing structure for general graph structured data called ρ-index that allows an effective processing of special path queries is presented. These special queries represent for example a search for all paths lying between two arbitrary vertices limited to a certain path length. The ρ-index is a multilevel balanced tree structure where each node is created with a certain graph transformation and described by modified adjacency matrix. Hence, ρ-index indexes all the paths to a predefined length l inclusive. The search algorithm is then able to find all the paths shorter than or having the length l and some of the paths longer then the predefined l lying between any two vertices in the indexed graph. The designed search algorithm exploits a special graph structure, a transcription graph, to compute the result using the ρ-index . We also present an experimental evaluation of the process of creating the ρ-index on graphs with different sizes and also a complexity evaluation of the search algorithm that uses the ρ-index.

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References

  1. Agrawal, R., Borgida, A., Jagadish, H.V.: Efficient management of transitive relationships in large data and knowledge bases. In: Proceedings of the 1989 ACM SIGMOD international conference on Management of data, pp. 253–262. ACM Press, New York (1989)

    Chapter  Google Scholar 

  2. 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)

    Article  MathSciNet  Google Scholar 

  3. Agrawal, R., Jagadish, H.V.: Hybrid transitive closure algorithms. In: McLeod, D., Sacks-Davis, R., Schek, H.-J. (eds.) Proceedings of 16th International Conference on Very Large Data Bases, Brisbane, Queensland, Australia, August 13-16, 1990, pp. 326–334. Morgan Kaufmann, San Francisco (1990)

    Google Scholar 

  4. An, Y., Janssen, J., Milios, E.E.: Characterizing and mining the citation graph of the computer science literature. In: Knowledge Information Systems, vol. 6, pp. 664–678. Springer, New York (2004)

    Google Scholar 

  5. Anyanwu, K., Sheth, A.: The ρ-operator: Enabling querying for semantic associations on the semantic web. In: Proceedings of the twelfth international conference on World Wide Web, pp. 690–699. ACM Press, New York (2003)

    Chapter  Google Scholar 

  6. Barton, S.: Indexing Graph Structured Data. PhD thesis, Faculty of Informatics, Masaryk University, Brno (May 2007)

    Google Scholar 

  7. Bartoň, S.: 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 (2004)

    Google Scholar 

  8. Bartoň, S., Zezula, P.: Rho-index - an index for graph structured data. In: 8th International Workshop of the DELOS Network of Excellence on Digital Libraries, pp. 57–64 (2005)

    Google Scholar 

  9. Brickley, D., Guha, R.V.: Resource Description Framework Schema specification (2000)

    Google Scholar 

  10. Cohen, E., Halperin, E., Kaplan, H., Zwick, U.: Reachability and distance queries via 2-hop labels. In: Proceedings of the 13th ACM-SIAM SODA, pp. 937–946 (2002)

    Google Scholar 

  11. Cronen-Townsend, S., Zhou, Y., Croft, W.B.: Predicting query performance. In: Proceedings of the 25th Annual International ACM SIGIR conference on Research and Development in Information Retrieval (SIGIR 2002), pp. 299–306 (August 2002)

    Google Scholar 

  12. Giles, C.L., Bollacker, K., Lawrence, S.: CiteSeer: An automatic citation indexing system. In: Witten, I., Akscyn, R., Shipman III, F.M. (eds.) Digital Libraries 98 - The Third ACM Conference on Digital Libraries, Pittsburgh, PA, June 23–26, pp. 89–98. ACM Press, New York (1998)

    Chapter  Google Scholar 

  13. Lassila, O., Swick, R.R.: Resource Description Framework: Model and Syntax specification (1999)

    Google Scholar 

  14. Manber, U., Myers, G.: Suffix arrays: a new method for on-line string searches. In: SODA 1990: Proceedings of the first annual ACM-SIAM symposium on Discrete algorithms. Society for Industrial and Applied Mathematics, Philadelphia, PA, USA, pp. 319–327 (1990)

    Google Scholar 

  15. Matono, A., Amagasa, T., Yoshikawa, M., Uemura, S.: An indexing scheme for RDF and RDF Schema based on suffix arrays. In: Proceedings of SWDB 2003, The first International Workshop on Semantic Web and Databases, Co-located with VLDB 2003 (2003)

    Google Scholar 

  16. Purdom, P.W.: A transitive closure algorithm. BIT 10, 76–94 (1970)

    Article  MATH  Google Scholar 

  17. Tarjan, R.E.: Depth first search and linear graph algorithms. SIAM Journal on computing, 146–160 (1972)

    Google Scholar 

  18. Tarjan, R.E.: Fast algorithms for solving path problems. J. ACM 28(3), 594–614 (1981)

    Article  MATH  MathSciNet  Google Scholar 

  19. Tarjan, R.E.: A unified approach to path problems. J. ACM 28(3), 577–593 (1981)

    Article  MATH  MathSciNet  Google Scholar 

  20. Thacker, S., Sheth, A., Patel, S.: Complex relationships for the semantic web. In: Fensel, D., Hendler, J., Liebermann, H., Wahlster, W. (eds.) Spinning the Semantic Web. MIT Press, Cambridge (2002)

    Google Scholar 

  21. Van Rijsbergen, C.J.: Information Retrieval, 2nd edition. Dept. of Computer Science, University of Glasgow (1979)

    Google Scholar 

  22. Warshall, S.: A theorem on boolean matrices. Journal of the ACM 9(1), 11–12 (1962)

    Article  MATH  MathSciNet  Google Scholar 

  23. Yan, X., Yu, P.S., Han, J.: Graph indexing based on discriminative frequent structure analysis. ACM Transactions on Database Systems 30(4), 960–993 (2005)

    Article  Google Scholar 

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Bartoň, S., Zezula, P. (2009). Indexing Structure for Graph-Structured Data. In: Zighed, D.A., Tsumoto, S., Ras, Z.W., Hacid, H. (eds) Mining Complex Data. Studies in Computational Intelligence, vol 165. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-88067-7_10

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  • DOI: https://doi.org/10.1007/978-3-540-88067-7_10

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-88066-0

  • Online ISBN: 978-3-540-88067-7

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