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A Reduction of Online Execution Time with Sparql Query Method on Mapreduce for Bigdata Applications

IJCSEC Front Page
Author(s):
Suganya.C, Raji.V, Kumaresan.A, Nanduja.R
Volume
4, Issue 3
Pages:
1423-1427
Year of Publication:
June,2016
International Journal of Computer Science and Engineering Communications
ISSN:
2347-8586
Citation: Suganya.C, Raji.V, Kumaresan.A, Nanduja.R."A Reduction of Online Execution Time with Sparql Query Method on Mapreduce for Bigdata Applications" International Journal of Computer Science and Engineering Communications,Vol.4,Issue.3.(2016):1423-1427. BibTex

@article{suganya2016areduction, author = {Suganya,C. Raji,V.Kumaresan,A. Nanduja,R}, title = {A Reduction of Online Execution Time with Sparql Query Method on Mapreduce for Bigdata Applications}, journal = {International Journal of Computer Science and Engineering Communications}, issue_date = {Jun 2016}, volume = {4}, number = {3}, month = {Jun}, year = {2016}, issn = {2347-8586}, pages = {1423-1427}, numpages = {5}, url = {http://www.scientistlink.com/ijcsec/2016/V4I314231427.html}, publisher = {Scientist Link Group of Publications}, address = {Chennai, India} }

DOI: Full Text Download
Abstract:
With the upcoming deluge amount of data the number of services are emerging on internet. The retrieval of user input from web is difficult . To overcome this challenges a new approach is proposed, which is the collaborative filtering (Club-CF) and description logic based matching technique is used to solve the matching problem. Its role aim at recruiting the similar services in the same clusters of recommended services collaboratively. This approach is achieved using two stages like all the available services divided into an small-scale cluster and then the collaborative filtering algorithm is imposed on clusters. Now the number of services in clusters which is comparatively much less than the services available on web. As a result thus approach helps to reduce the online execution time of collaborative filtering. So the user recommended services were easily extracted from the database.

Keywords:Description logic, Collaborative filtering, Feature selection, Clustering

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