Social Mining to Progress the Computational Efficiency using Mapreduce

Sudhir Tirumalasetty

Social Mining to Progress the Computational Efficiency using Mapreduce

Sudhir Tirumalasetty * §

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§ Vasireddy Venkatadri Institute of Technology

Social Mining to Progress the Computational Efficiency using Mapreduce

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Abstract

Graphs are widely used in large scale social network analysis. Graph mining increasingly important in modelling complicated structures such as circuits, images, web, biological networks and social networks. The major problems occur in this graph mining are computational efficiency (CE) and frequent subgraph mining (FSM). Computational Efficiency describes the extent to which the time, effort or efficiency which use computing technology in information processing. Frequent Sub graph Mining is the mechanism of candidate generation without duplicates. FSM faces the problem on counting the instances of the patterns in the dataset and counting of instances for graphs. The main objective of this project is to address CE and FSM problems. The paper cited in the reference proposes an algorithm called Mirage algorithm to solve queries using subgraph mining. The proposed work focuses on enhancing An Iterative Map Reduce based Frequent Subgraph Mining Algorithm (MIRAGE) to consider optimum computational efficiency. The test data to be considered for this mining algorithm can be from any domains such as medical, text and social data’s (twitter). The major contributions are: an iterative MapReduce based frequent sub graph mining algorithm called MIRAGE used to address the frequent sub graph mining problem.

References

3 Cites in Article

Reference Format

A Mansurul,Mohammad Bhuiyan,Al Hasan (2013). MIRAGE: An Iterative MapReduce based Frequent Subgraph Mining Algorithm.
Yi-Chen Lo,Hung Chelai,Cheng-Te Li,Shou-De Lin (2013). Mining and Generating Large Scaled Social Networks via MapReduce.
Grant Sabasehrish,Pengju Mackey,Jun Shang,John Wang,Bent (2013). Supporting HPC Analytics Applications with Access Patterns Using Data Restructuringand Data-Centric Scheduling TechniquesinMapReduce.

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Funding

No external funding was declared for this work.

Conflict of Interest

The authors declare no conflict of interest.

Ethical Approval

No ethics committee approval was required for this article type.

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Not applicable for this article.

How to Cite This Article

Sudhir Tirumalasetty. 2015. "Social Mining to Progress the Computational Efficiency using Mapreduce". Global Journal of Computer Science and Technology - C: Software & Data Engineering GJCST-C Volume 15 (GJCST Volume 15 Issue C4).

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GJCST Volume 15 Issue C4

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Journal Specifications

Crossref Journal DOI 10.17406/gjcst

Print ISSN 0975-4350

e-ISSN 0975-4172

Keywords

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Classification

D.2.12 I.3.3 H.2.8

Submission ReceivedDecember 11, 2014
Peer Review Double Blind
Handling Editor
Accepted January 2, 2015
Published January 15, 2015

Version of record

v1.2

Issue date

June 19, 2015

Language

English

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Article Matrices

Total Score: 148

Country: India

Subject: Global Journal of Computer Science and Technology

Authors: Sudhir Tirumalasetty, Dr.SreenivasaReddy Edara, Arunaj Jadda (PhD/Dr. count: 1)

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Publish Date: 2015 01, Thu

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Total Views: 2.3K

Total Downloads: 205

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