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Reseach Article

Examining the Performance of Vertical Fragmentation using FP-MAX Algorithm

by Nidhi Thakur, Balwant Ram
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 116 - Number 23
Year of Publication: 2015
Authors: Nidhi Thakur, Balwant Ram
10.5120/20514-2691

Nidhi Thakur, Balwant Ram . Examining the Performance of Vertical Fragmentation using FP-MAX Algorithm. International Journal of Computer Applications. 116, 23 ( April 2015), 43-48. DOI=10.5120/20514-2691

@article{ 10.5120/20514-2691,
author = { Nidhi Thakur, Balwant Ram },
title = { Examining the Performance of Vertical Fragmentation using FP-MAX Algorithm },
journal = { International Journal of Computer Applications },
issue_date = { April 2015 },
volume = { 116 },
number = { 23 },
month = { April },
year = { 2015 },
issn = { 0975-8887 },
pages = { 43-48 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume116/number23/20514-2691/ },
doi = { 10.5120/20514-2691 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T22:58:00.660450+05:30
%A Nidhi Thakur
%A Balwant Ram
%T Examining the Performance of Vertical Fragmentation using FP-MAX Algorithm
%J International Journal of Computer Applications
%@ 0975-8887
%V 116
%N 23
%P 43-48
%D 2015
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Today’s business Environment has an increasing need for consistent, scalable, reliable and accessible information which grows steadily.The purpose of this work is to analyse the performance of Vertical Fragmentation on large as well as small database such as educational database, data warehouses, medical databases. Vertical Fragmentation has an important impact in improving the performance of modern applications like document management, multimedia and hypermedia applications. With vertical partitioning, the disk access can be reduced by minimizing the access to irrelevant instance variables when executing the queries. In Present work FP-MAX data mining algorithm is used to extract frequent item set attributes of a large database table. The frequent accessed instance variables are grouped called Vertical fragments. The cost model is applied on Vertical fragments. The results are analyzed for small as well as large datasets in form of Max memory usage, Total time, cost and Frequent itemset count.

References
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Index Terms

Computer Science
Information Sciences

Keywords

Database Ivertical fragmentation frequent item set data mining.