International Journal of Intelligent Systems and Applications (IJISA)
IJISA Vol. 9, No. 10, 8 Oct. 2017
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Formal Validation of Data Warehouse Complexity Metrics using Distance Framework
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Author(s)
Index Terms
Distance framework, metrics, theoretical validation, data warehouse quality, multidimensional models
Abstract
Data Warehouse is the cornerstone for organizations that base their strategic decisions on the large scale processing of numerical data. The success of the organization depends on these decisions and hence it becomes extremely important to have a quality data warehouse. Conceptual models have been widely recognized as a key determinant of data warehouse quality during the early stages of design. Recently, metrics have been proposed by authors based on hierarchies to quantify the complexity and inturn quality of the conceptual models of data warehouse. They have formally corroborated the measures against Briand’s property based framework to ensure their validity. However, Briand’s set of properties for software measures are a set of necessary but not sufficient measure axioms. They are advantageous to refute software metrics but not to validate them. Thus, we focus on the theoretical validation of the data warehouse conceptual model metrics using the Distance framework whose sufficiency is ensured by the measurement theory. The results indicate that the metrics are valid measures of the complexity of data warehouse conceptual models. Besides, validation by Distance framework assures that the metrics are in the ratio scale which further aids in data analysis.
Cite This Paper
Gargi Aggarwal, Sangeeta Sabharwal, "Formal Validation of Data Warehouse Complexity Metrics using Distance Framework", International Journal of Intelligent Systems and Applications(IJISA), Vol.9, No.10, pp.49-56, 2017. DOI:10.5815/ijisa.2017.10.06
Reference
[1]W. H. Inmon, Building the Data Warehouse. Wiley, 2005.
[2]N. T. Debevoise, The data warehouse method. Prentice Hall, 1998.
[3]C. Calero, C. Pascual, M. Piattini, and M. A. Serrano, “Towards Data Warehouse Quality Metrics,” in Proceedings of the International Workshop on Design and Management of Data Warehouses, 2001, pp. 1–10.
[4]C. Calero, M. Piattini, and M. Genero, “Method for Obtaining Correct Metrics,” in Third International Conference on Enterprise Information Systems, 2001, pp. 779–784.
[5]L. C. Briand, S. Morasca, and V. R. Basili, “Property-based software engineering measurement,” IEEE Trans. Softw. Eng., vol. 22, no. 1, pp. 68–86, 1996.
[6]E. J. Weyuker, “Evaluating Software Complexity Measures,” IEEE Trans. Softw. Eng., vol. 14, no. 9, pp. 1357–1365, 1988.
[7]G. Poels and G. Dedene, “Distance-based software measurement: Necessary and sufficient properties for software measures,” Inf. Softw. Technol., vol. 42, no. 1, pp. 35–46, 2000.
[8]H. Zuse, A Framework of Software Measurement. Walter de Gruyter, 1998.
[9]A. Gosain, S. Nagpal, and S. Sabharwal, “Validating dimension hierarchy metrics for the understandability of multidimensional models for data warehouse,” IET Softw., vol. 7, no. 2, pp. 93–103, 2013.
[10]P. Suppes, M. Krantz, R. Luce, and A. Tversky, Foundations of Measurement. New York: Academic Press, 1989.
[11]M. A. Serrano, C. Calero, H. A. Sahraoui, and M. Piattini, “Empirical studies to assess the understandability of data warehouse schemas using structural metrics,” Softw. Qual. J., vol. 16, no. 1, pp. 79–106, 2008.
[12]G. Berenguer, R. Romero, J. Trujillo, M. Serrano, and M. Piattini, “A set of quality indicators and their corresponding metrics for conceptual models of data warehouses,” in Data Warehousing and Knowledge Discovery, 2005, pp. 95–104.
[13]S. S. Cherfi and N. Prat, “Multidimensional Schemas Quality : Assessing and Balancing Analyzability and Simplicity,” in Proceedings of ER Workshops, Springer LNCS, 2003, pp. 140–151.
[14]M. Serrano, C. Calero, J. Trujillo, S. Lujan, and M. Piattini, “Empirical validation of metrics for conceptual models of data warehouse,” in 16th International Conference on Advanced Information Systems Engineering (CAISE’04), 2004, pp. 506–520.
[15]M. Serrano, J. Trujillo, C. Calero, and M. Piattini, “Metrics for data warehouse conceptual models understandability,” Inf. Softw. Technol., vol. 49, no. 8, pp. 851–870, 2007.
[16]S. Nagpal, A. Gosain, and S. Sabharwal, “Theoretical and empirical validation of comprehensive complexity metric for multidimensional models for data warehouse,” Int. J. Syst. Assur. Eng. Manag., vol. 4, no. 2, pp. 193–204, 2013.
[17]S. Sabharwal, S. Nagpal, and G. Aggarwal, “Coupling metrics for object-oriented data warehouse design,” in Computing for Sustainable Global Development (INDIACom), 2015 2nd International Conference on, 2015, pp. 918–922.
[18]M. Genero, G. Poels, and M. Piattini, “Defining and validating metrics for assessing the understandability of entity-relationship diagrams,” Data Knowl. Eng., vol. 64, no. 3, pp. 534–557, 2008.
[19]P. Tripathi, M. Kumar, and N. Shrivastava, “Theoretical validation of quality metrics of Indian e-commerce domain,” in 2009 2nd International Conference on Computer, Control and Communication, 2009, pp. 1–7.
[20]P. Rossi and G. Fernandez, “Definition and validation of design metrics for distributed applications,” in Proceedings. 5th International Workshop on Enterprise Networking and Computing in Healthcare Industry (IEEE Cat. No.03EX717), 2003, pp. 124–132.
[21]A. O. Bajeh, S. Basri, and L. T. Jung, “A theoretical validation of the number of polymorphic methods as a complexity metric,” in 2014 International Conference on Computer and Information Sciences (ICCOINS), 2014, pp. 1–6.
[22]L. Muñoz, J. N. Mazón, and J. Trujillo, “A family of experiments to validate measures for UML activity diagrams of ETL processes in data warehouses,” Inf. Softw. Technol., vol. 52, no. 11, pp. 1188–1203, 2010.