Frequent Subgraph Mining for Graph based Tamil Bibliographic Big Data Analytics
Elangovan. G1, Kavya. G2, Kalpana. A.V3, Jagadish Kumar. N4, Anwar Basha. H5
1Elangovan G, Assistant Professor, Department of Computer Science & Engineering, Velammal Institute of Technology, Chennai (Tamil Nadu), India.
2Dr. Kavya G, Professor, Department of Electronics & Communication Engineering, S.A Engineering College, Chennai (Tamil Nadu), India.
3Kalpana A V, Assistant Professor, Department of Computer Science & Engineering, Velammal Institute of Technology, Chennai (Tamil Nadu), India.
4Jagadish Kumar. N, Department of Computer Science and Engineering, Anna University, Chennai (Tamil Nadu), India.
5Anwar Basha. H, Department of Computer Science and Engineering, Anna University, Chennai (Tamil Nadu), India.
Manuscript received on 23 November 2019 | Revised Manuscript received on 04 December 2019 | Manuscript Published on 14 December 2019 | PP: 222-227 | Volume-9 Issue-1S November 2019 | Retrieval Number: A10461191S19/2019©BEIESP | DOI: 10.35940/ijitee.A1046.1191S19
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open-access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: Data analysis can be done in more effectively, when they are represented in the form of graphs. Especially, Frequent Subgraph Mining (FSM) is an important technique for extracting similar patterns in the graphs. Normally, things have been assumed as the graph data we are taking will fit in main memory for their processing. But, as the data grow higher and higher, they will not fit in main memory, rather they need a special framework called MapReduce to put them in a distributed fashion and to process. Many Frequent Subgraph Mining (FSM) algorithms are changing their faces to adopt the MapReduce programming paradigm. Using FSM-H, analysis had been performed on various graph based data like molecular structures, viral patterns etc., Even DBLP i.e., the computer science bibliography data have also been analyzed using this pattern extraction technique. Whereas the details of Tamil Journals and Publications are kept hidden and not available widely to do research on them, for getting their insights to improve the number and quality of the journals; and to give some input for the authors interested to work on Tamil research. In this work, we collected the Tamil journals details from the available data sources and extracted essential patterns using Frequent Subgraph Mining Technique. Also, we presented a detailed statistical analytics on certain frequently happening Tamil Journals and Conferences.
Keywords: Especially, Frequent Subgraph Mining, MapReduce Programming paradigm, DBLP.
Scope of the Article: Big Data Analytics