A Framework for SQL-Based Mining of Large Graphs on Relational Databases

  • Sriganesh Srihari
  • Shruti Chandrashekar
  • Srinivasan Parthasarathy
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6119)


We design and develop an SQL-based approach for querying and mining large graphs within a relational database management system (RDBMS). We propose a simple lightweight framework to integrate graph applications with the RDBMS through a tightly-coupled network layer, thereby leveraging efficient features of modern databases. Comparisons with straight-up main memory implementations of two kernels - breadth-first search and quasi clique detection - reveal that SQL implementations offer an attractive option in terms of productivity and performance.


Graph mining SQL-based approach Relational databases 


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  1. 1.
    Aggarwal, C., Yan, X., Yu, P.S.: GConnect: A connectivity index for massive disk-resident graphs. In: Very Large Databases (VLDB), vol. 2, pp. 862–873 (2009)Google Scholar
  2. 2.
    Chen, W., et al.: Scalable mining of large disk-based graph databases. In: ACM Knowledge Discovery and Data Mining (SIGKDD), pp. 316–325 (2004)Google Scholar
  3. 3.
    Chakravarthy, S., Beera, R., Balachandran, R.: DB-Subdue: Database approach to graph mining. In: Dai, H., Srikant, R., Zhang, C. (eds.) PAKDD 2004. LNCS (LNAI), vol. 3056, pp. 341–350. Springer, Heidelberg (2004)Google Scholar
  4. 4.
    Chakravarthy, S., Pradhan, S.: DB-FSG: An SQL-based approach for frequent subgraph mining. In: Bhowmick, S.S., Küng, J., Wagner, R. (eds.) DEXA 2008. LNCS, vol. 5181, pp. 684–692. Springer, Heidelberg (2008)CrossRefGoogle Scholar
  5. 5.
    Jin, R., et al.: Efficiently answering reachability queries on very large directed graphs. In: ACM Management of Data (SIGMOD), pp. 595–608 (2008)Google Scholar
  6. 6.
    Mishra, P., Chakravarthy, S.: Performance evaluation and analysis of k-way join variants for association rule mining. In: James, A., Younas, M., Lings, B. (eds.) BNCOD 2003. LNCS, vol. 2712, pp. 95–114. Springer, Heidelberg (2003)CrossRefGoogle Scholar
  7. 7.
  8. 8.
  9. 9.
    Sarawagi, S., Thomas, S., Agarwal, R.: Integrating mining with relational database systems: Alternatives and implications. In: ACM Management of Data (SIGMOD), pp. 343–354 (1998)Google Scholar
  10. 10.
    Srihari, S., Ng, H.K., Ning, K., Leong, H.W.: Detecting hubs and quasi cliques in scale-free networks. In: IEEE Pattern Recognition (ICPR), pp. 1–4 (2008)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2010

Authors and Affiliations

  • Sriganesh Srihari
    • 1
  • Shruti Chandrashekar
    • 2
  • Srinivasan Parthasarathy
    • 3
  1. 1.School of ComputingNational University of SingaporeSingapore
  2. 2.New Jersey Institute of TechnologyNewark
  3. 3.The Ohio State UniversityColumbus

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