An Efficient Map Reduce-Based System to find Userlikeness on Social Networks

D. Ravikiran; Dr. S.V.N Srinivasu

An Efficient Map Reduce-Based System to find Userlikeness on Social Networks

D. Ravikiran * §

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Dr. S.V.N Srinivasu §

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§ Acharya Nagarjuna University

An Efficient Map Reduce-Based System to find Userlikeness on Social Networks

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Abstract

Day to day Social network information growth pursues an exponential pattern, and Present DB management systems cannot manage efficiently such a huge volume of data. It is essential to employ a “big data” solution for Social network problems. One of the most important problems in Social network is finding User likeness (ULi). Current methods for finding ULi are not flexible and do not sustain all data sources, nor can them accomplish user necessities for a query tool. In this paper, we propose a reliable and data available method to solve ULi problems over MapReduce design. RiDaULi supports storage and retrieval of all kinds of data sources in an appropriate manner. The dynamic nature of the proposed method helps users to define conditions on all entered fields. Our assessment shows that we can use this method as high confidence in less execution time.

References

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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.

Data Availability

Not applicable for this article.

How to Cite This Article

D. Ravikiran, Dr. S.V.N Srinivasu. 2015. "An Efficient Map Reduce-Based System to find Userlikeness on Social Networks". Global Journal of Computer Science and Technology - C: Software & Data Engineering GJCST-C Volume 15 (GJCST Volume 15 Issue C7).

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Journal Specifications

Crossref Journal DOI 10.17406/gjcst

Print ISSN 0975-4350

e-ISSN 0975-4172

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Classification

GJCST-C Classification K.6.3

D.2.12

Submission ReceivedDecember 6, 2014
Peer Review Double Blind
Handling Editor
Accepted January 4, 2015
Published November 6, 2015

Version of record

v1.2

Issue date

November 6, 2015

Language

English

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Article Matrices

Total Score: 107

Country: India

Subject: Global Journal of Computer Science and Technology - C: Software & Data Engineering

Authors: D. Ravikiran, Dr. S.V.N Srinivasu (PhD/Dr. count: 1)

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