Map Reduce Based Optimized Frequent Subgraph Mining Algorithm for Large Graph Database
Sadhana Priyadarshini1, Sireesha Rodda2
1Ms. Sadhana Priyadarshini*, Ph.d. scholar, Department of Computer Science and Engineering, GITAM (Deemed to be University), Vishakhapatnam, India
2Dr. Sireesha Rodda, is a Professor in the Department of Computer Science & Engineering, GITAM (Deemed to be University) Vishakhapatnam, India
Manuscript received on January 26, 2020. | Revised Manuscript received on February 05, 2020. | Manuscript published on February 30, 2020. | PP: 3131-3139 | Volume-9 Issue-3, February 2020. | Retrieval Number: C6141029320/2020©BEIESP | DOI: 10.35940/ijeat.C6141.029320
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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: Distributed System, plays a vital role in Frequent Subgraph Mining (FSM) to extract frequent subgraph from Large Graph database. It help to reduce in memory requirements, computational costs as well as increase in data security by distributing resources across distributed sites, which may be homogeneous or heterogeneous. In this paper, we focus on the problem related complexity of data arises in centralized system by using MapReduce framework. We proposed a MapReduced based Optimized Frequent Subgrph Mining (MOFSM) algorithm in MapReduced framework for large graph database. We also compare our algorithm with existing methods using four real-world standard datasets to verify that better solution with respect to performance and scalability of algorithm. These algorithms are used to extract subgraphs in distributed system which is important in real-world applications, such as computer vision, social network analysis, bio-informatics, financial and transportation network.
Keywords: Distributed System, subgraph, support count, Graph Database, Mapper, and Reducer.