Geodesic
Applying parallel DBMS for very large graph mining
Vestnik Ûžno-Uralʹskogo gosudarstvennogo universiteta. Seriâ Vyčislitelʹnaâ matematika i informatika, no. 2 (2012), pp. 127-132 Cet article a éte moissonné depuis la source Math-Net.Ru

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Graph partitioning is an interesting topic in graph mining, that comes into use for some theoretical and practical problems (graph coloring, integrated curcuit desing, finite element modeling, etc.). The existing serial and parallel algorithms suppose that the graph being analyzed can fit into main memory along with all the intermediate data, so they cannot be applied for very large graphs. We introduce a new way of partitining – using the parallel relational DBMS PargreSQL that is based on open-source PostgreSQL DBMS.
Keywords: data mining, graph partitioning, parallel DBMS. 
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K. S. Pan. Applying parallel DBMS for very large graph mining. Vestnik Ûžno-Uralʹskogo gosudarstvennogo universiteta. Seriâ Vyčislitelʹnaâ matematika i informatika, no. 2 (2012), pp. 127-132. http://geodesic.mathdoc.fr/item/VYURV_2012_2_a10/

[1] C. Ordonez, S.K. Pitchaimalai, “Bayesian Classifiers Programmed in SQL”, IEEE Transactions on Knowledge and Data Engineering, 22:1 (2010), 139–144

[2] C. Ordonez, “Integrating K-Means Clustering with a Relational DBMS Using SQL”, IEEE Transactions on Knowledge and Data Engineering, 18:2 (2006), 188–201

[3] C.S. Pan, M.L. Zymbler, “A Parallel Algorithm for Market Basket Analysis on Cell Processor”, Bulletin of the South Ural State University. Series “Mathematical Modeling and Programming”, 2010, no. 5, 48–57

[4] B.W. Kernighan, S. Lin, “An Efficient Heuristic Procedure for Partitioning Graphs”, The Bell System Technical Journal, 49:1 (1970), 291–307

[5] C.C. Aggarwal, H. Wang, “Managing and Mining Graph Data”, Advances in Database Systems, 40 (2010), 608–608, Springer

[6] R.M. Miniakhmetov, “Integrating Fuzzy c-Means Clustering with PostgreSQL”, Proceedings of the Institute for System Programming of RAS, 21 (2011), 263–276

[7] R.M. Miniakhmetov, M.L. Zymbler, “Embedding Fuzzy c-Means into PostgreSQL”, Computational Methods and Programming: New Computational Technologies (Digital Scientific Journal), 13 (2012), 46–52

[8] S. Chakravarthy, S. Pradhan, “DB-FSG: An SQL-Based Approach for Frequent Subgraph Mining”, Proceedings of the 19th International Conference on Database and Expert Systems Applications DEXA 2008 (Turin, Italy, September 1–5), v. 13, Springer, 2008, 684–692

[9] S. Srihari, S. Chandrashekar, S. Parthasarathy, “A Framework for SQL-Based Mining of Large Graphs on Relational Databases”, Advances in Knowledge Discovery and Data Mining, v. 6119, Lecture Notes in Computer Science, 2010, 160–167 | DOI

[10] L.B. Sokolinsky, “Survey of Architectures of Parallel Database Systems”, Programming and Computer Software, 30:6 (2004), 337–346

[11] A.V. Lepikhov, L.B. Sokolinsky, “Query Processing in a DBMS for Cluster Systems”, Programming and Computer Software, 36:4 (2010), 205–215

[12] A.V. Lepikhov, L.B. Sokolinsky, “Query Evaluation Techniques for Cluster Database Systems”, Advances in Databases and Information Systems, 14th East European Conference on Advances in Databases and Information Systems (ADBIS 2010, Novi Sad, Serbia, September 20-24), v. 6295, Lecture Notes in Computer Science, Springer, Berlin, Heidelberg, 2010, 351–362 | DOI

[13] C.S. Pan, M.L. Zymbler, “Development of a Parallel Database Management System on the Basis of Open-Source PostgreSQL DBMS”, Bulletin of the South Ural State University “Mathematical Modeling and Programming”, 18:12 (2012), 112–120