Call for Paper - July 2022 Edition
IJCA solicits original research papers for the July 2022 Edition. Last date of manuscript submission is June 20, 2022. Read More

Modeling Data Warehouse using Quality Metrics:The Need of Software Process

Print
PDF
IJCA Special Issue on Confluence 2012 - The Next Generation Information Technology Summit
© 2012 by IJCA Journal
CONFLUENCE - Number 1
Year of Publication: 2012
Authors:
Naveen Dahiya
Vishal Bhatnagar
Manjit Singh

Naveen Dahiya, Vishal Bhatnagar and Manjit Singh. Article: Modeling Data Warehouse using Quality Metrics:The Need of Software Process. IJCA Special Issue on Confluence 2012 - The Next Generation Information Technology Summit CONFLUENCE(1):28-31, September 2012. Full text available. BibTeX

@article{key:article,
	author = {Naveen Dahiya and Vishal Bhatnagar and Manjit Singh},
	title = {Article: Modeling Data Warehouse using Quality Metrics:The Need of Software Process},
	journal = {IJCA Special Issue on Confluence 2012 - The Next Generation Information Technology Summit},
	year = {2012},
	volume = {CONFLUENCE},
	number = {1},
	pages = {28-31},
	month = {September},
	note = {Full text available}
}

Abstract

Data warehouses play a powerful role in decision making in the organizations. Data warehouse provides most accurate and relevant information to improve strategic decisions making process. There exist several approaches for data warehouse design and their quality assurance to help designers choose among alternative schemas that are semantically equivalent. This paper focuses on the quality of the conceptual models of the data warehouses. The process of metrics creation is explained followed by validation of metrics along with a discussion on previously proposed metrics for data warehouse conceptual models.

References

  • Serrano, M. ,Trujillo, J. ,Calero, C. , & Piattini, M. Metrics For Data warehouse Conceptual Models Understandability, Information and software technology, science direct,49,2007.
  • Basili, V. , Weiss, D. , A Methodology For Collecting Valid Software Engineering Data, IEEE Transaction on Software Engineering, 1984.
  • Calero C. , Piattini, M. , Pascual, C. , & Serrano, M. Towards Data warehouse Quality Metrics, International Workshop on Design and Management of Data Warehouses (DMDW'01), 2001.
  • Weyuker, E. , Evaluating Software Complexity Measures, IEEE Transactions on Software Engineering, 1988.
  • Briand, L. , Morasca, S. & Basili, V. , Property-based Software Engineering Measurement, IEEE Transcations on Software Engineering, 1996.
  • Piattini, M. , Genero, M. , & Jimenez, L. , Metrics Based Approach for Predicting Conceptual Data Model Maintainability, International Journal of Software Engineering and Knowledge Engineering vol:11,703-729, 2001.
  • Whitmire, S. , Object Oriented Design Measurement, Ed. Wiley, 1997.
  • Zuse, H. , A Framework of Software Measurement, Walter de Gruyter, 1998.
  • Poels, G. , Dedene, G. , DISTANCE: A Framework Software Measure Construction, Research ReportDTEW9937 Dept. Applied Economics Katholieke University Leuven, Belgium, 1999.
  • Sahraoui, H. , Serrano, M. , Calero, C. & Piattini, M. , Empirical Studies to Assess the Understandability of Data warehouse Schemas Using Structural Metrics, Software Qual J, 2008.
  • Serrano, M. , Calero, C. , & Piattini, M. , Validating metrics for data warehouses. IEEE Proceedings SOFTWARE, 149(5), 161–166, 2002.
  • Serrano, M. , Calero, C. , & Piattini, M. , An experimental replication with warehouse metrics. International Journal of Data Warehousing & Mining, 1(4), 1–21, 2005.
  • Grosser, D. , Sahraoui, H. A. , & Valtchev, P. , An analogy-based approach for predicting design stability of Java classes. In International Symposium on Software Metrics,252–262, 2003.
  • ilson, D. , & Martinez, T. , Improved heterogeneous distance functions, Journal of Artificial Intelligence Research, 6, 1–34,1997
  • Godin, R. , Mineau, G. , Missaoui, R. , St-Germain, M. , & Faraj, N. , Applying concept formation methods to software reuse. International Journal of Knowledge Engineering and Software Engineering, 5(1), 119–142,1995.
  • Ramoni, M. , & Sebastiani, P. , Bayesian methods for intelligent data analysis. In: M. Berthold & D. J. ,Hand (Eds. ), An introduction to intelligent data analysis, 1999.
  • R. Gray, B. Carey, N. McGlynn and A. Pengelly, Design metrics for database systems, BT Technology J. , 9(4), 69-79, 1991.
  • S. Kesh, Evaluating the Quality of Entity Relationship Models, Information and Software Technology, 37(12), 681-689, 1995.
  • L. Moody, Metrics For Evaluating the Quality of Entity Relationship Models, Proceedings of the Seventeenth International Conference on Conceptual Modeling (ER ´98), 213-225, 1998.
  • M. Genero, M. Piattini, C. Calero and M. Serrano, Assurance of Conceptual Data Model Quality Based on Early Measures, IEEE Transactions on Software Engineering, 2001.
  • Boman, M. , Bubenko, J. , Johannesson, P. & Wangler, B. , Conceptual Modeling, Prentice Hall, 1997.
  • Serrano, M. , Definition of a Set of Metrics for Assuring Data warehouse Quality, University of Castilla, 2004.