Enhancing Sub Graph Matching With Set Correlation Technique in Large Graph Database

  • Authors

    • Bharti Nana Durgade
    • Prof. K. S. Kadam
    2018-09-22
    https://doi.org/10.14419/ijet.v7i4.5.20000
  • Sub Graph, Similarity, Correlation, Pattern Identification
  • As the increase of users in social networking sites gives rise to complex relation between the users, which often leads to understand the group of users with similar taste. Now days, this is a one of the growing research area to find the similar users in the relational graph database. Many systems are been introduced to identify the matching sub graphs using similarity between the users. This often yields not much appropriate results due to strict similarity measures. So proposed system uses a technique of identifying correlation between the users for the fired query using pattern identification by incorporating frequent pattern analysis and Pearson correlation which is catalyzed by strong pruning techniques.

     

     

  • References

    1. [1] B. Cui, H. Mei and B. C. Ooi, “Big data: The driver for innovation in databases,†Nat. Sci. Rev., vol. 1, no. 1, pp. 27–30, 2014.

      [2] J. Cheng, J. X. Yu, B. Ding, P. S. Yu, and H. Wang, “Fast graph Pattern matching,†in Proc. Int. Conf. Data Eng., 2008, pp.913–922.

      [3] X. Zhu, S. Song, X. Lian, J. Wang, and L. Zou, “Matching heterogeneousevent data,†in Proc. ACM SIGMOD Int. Conf. Manage. Data,2014, pp. 1211–1222.

      [4] P. Zhao and J. Han, “On graph query Endowment, vol. 3, nos. 1/2, optimization in large networks,†Proc. VLDB pp. 340–351, 2010.

      [5] Y. Tian and J. M. Patel, “Tale: A tool for approximate large Graph matching,†in Proc. 24th Int. Conf. Data Eng., 2008, pp. 963–972.

      [6] P.-N. Tan, V. Kumar, and J. Srivastava. Selecting the right Interestingness measure for association patterns. In KDD,pages 32– 41, 2002.

      [7] J. R. Ullmann, “An algorithm for subgraph isomorphism,†J. ACM, vol. 23, no. 1, pp. 31–42, 1976.

      [8] L. P. Cordella, P. Foggia, C. Sansone, and M. Vento, “A (sub)graph isomorphism algorithm for matching large graphs,†IEEE Trans.Pattern Anal. Mach. Intell., vol. 6, no. 10, pp. 1367– 1372, Oct. 2004.

      [9] S. Zhang, S. Li, and J. Yang, “Gaddi: Distance index based subgraph matching in biological networks,†in Proc. 12th Int.Conf. Extending Database Technol.:Adv. Database Technol., 2009, pp. 192– 203.

      [10] Y. Tian, R. C. McEachin, C. Santos, D. J. States, and J. M. Patel ,“Saga: A subgraph matching tool for biological graphs,†Bioinformatics, vol.23, no. 2, pp. 232–239, 2007.

      [11] M. Hadjieleftheriou, A. Chandel, N. Koudas, and D. Srivastava, “Fast indexes and algorithms for set similarity selection queries,†in Proc. 24th Int. Conf. Data Eng.,pp.2008, 267–276.

      [12] L. Zou, L. Chen, and Y. Lu, “Top-k subgraph matching query in a large graph,†in Proc. ACM 1st PhD Workshop CIKM, 2007, pp. 139– 146.

      [13] Liang Hong, Lei Zou, Xiang Lian and Philip S. Yu, “Subgraph Matching with Set Similarity in a Large Graph Database†IEEE Transactions on Knowledge and Data Engineering, Vol.27, No.9, September 2015

  • Downloads

  • How to Cite

    Nana Durgade, B., & K. S. Kadam, P. (2018). Enhancing Sub Graph Matching With Set Correlation Technique in Large Graph Database. International Journal of Engineering & Technology, 7(4.5), 16-19. https://doi.org/10.14419/ijet.v7i4.5.20000