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Validation of Object Oriented Metrics for Evaluating Understandability of Data Warehouse Models

by Jaspreeti Singh, Srishti Vashishtha
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 118 - Number 13
Year of Publication: 2015
Authors: Jaspreeti Singh, Srishti Vashishtha
10.5120/20806-3512

Jaspreeti Singh, Srishti Vashishtha . Validation of Object Oriented Metrics for Evaluating Understandability of Data Warehouse Models. International Journal of Computer Applications. 118, 13 ( May 2015), 26-33. DOI=10.5120/20806-3512

@article{ 10.5120/20806-3512,
author = { Jaspreeti Singh, Srishti Vashishtha },
title = { Validation of Object Oriented Metrics for Evaluating Understandability of Data Warehouse Models },
journal = { International Journal of Computer Applications },
issue_date = { May 2015 },
volume = { 118 },
number = { 13 },
month = { May },
year = { 2015 },
issn = { 0975-8887 },
pages = { 26-33 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume118/number13/20806-3512/ },
doi = { 10.5120/20806-3512 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T23:01:36.732946+05:30
%A Jaspreeti Singh
%A Srishti Vashishtha
%T Validation of Object Oriented Metrics for Evaluating Understandability of Data Warehouse Models
%J International Journal of Computer Applications
%@ 0975-8887
%V 118
%N 13
%P 26-33
%D 2015
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Datawarehouse has a key role in formulating strategic decisions thus it is very essential to maintain its quality. Metrics have been generally used to direct designers to develop quality data models. Numerous researchers have proposed metrics for multidimensional models for datawarehouse. These metrics are required to be empirically validated to prove their practical utility. Empirical validation of the object oriented metrics for multidimensional models for data warehouses at a conceptual level is presented in the paper. Quality attribute understandability is assessed through various combinations of metrics. Univariate and Multiple linear regression analysis have been used in this paper for computing the multidimensional models quality. The results show that these metrics may be considered as key indicators for quality of multidimensional data models.

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Index Terms

Computer Science
Information Sciences

Keywords

Datawarehouse Metrics Multidimensional models Quality attributes

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