Abstract
The quality of Data Warehouses is absolutely relevant for organizations in the decision making process. The sooner we can deal with quality metrics (i.e. conceptual modelling), the more willing we are in achieving a data warehouse (DW) of a high quality. From our point of view, there is a lack of more objective indicators (metrics) to guide the designer in accomplishing an outstanding model that allows us to guarantee the quality of these data warehouses. However, in some cases, the goals and purposes of the proposed metrics are not very clear on their own. Lately, quality indicators have been proposed to properly define the goals of a measurement process and group quality measures in a coherent way. In this paper, we present a framework to design metrics in which each metric is part of a quality indicator we wish to measure. In this way, our method allows us to define metrics (theoretically validated) that are valid and perfectly measure our goals as they are defined together a set of well defined quality indicators.
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References
Abelló, A., Samos, J., Saltor, F.: YAM2 (Yet Another Multidimensional Model): An extension of UML. In: International Database Engineering & Applications Symposium, IDEAS 2002 (2002)
Bouzeghoub, M., Kedad, Z.: Quality in Data Warehousing. In: Information and database quality, Kluwer Academic Publishers, Dordrecht (2002)
Briand, L., Morasca, S., Basili, V.: Property-Based Software Engineering Measurement. IEEE Transactions on Software Engineering 22(1), 68–86 (1996)
Briand, L., Wüst, J., Lounis, H.: A Comprehensive Investigation of Quality Factors in Object-Oriented Designs: an Industrial Case Study. In: 21st International Conference on Software Engineering, Los Angeles, California (1999)
Calero, C., Piattini, M., Genero, M.: Method for obtaining correct metrics. In: 3rd International Conference on Enterprise and Information Systems, ICEIS 2001 (2001)
Cantone, G., Donzelli, P.: Production and maintenance of software measurement models. Journal of Software Engineering and Knowledge Engineering 5, 605–626 (2000)
Goethert, W., Siviy, J.: Applications of the Indicator Template for Measurement and Analysis Initiative, Technical Note CMU/SEI-2004-TN-024 (2004)
Inmon, W.H.: Building the Data Warehouse, 3rd edn. John Wiley and Sons, USA (2002)
ISO/IEC, 9126-1: Software Engineering - Product quality - Part 1: Quality model (2001)
Jarke, M., Lenzerini, M., Vassiliou, Y., Vassiliadis, P.: Fundamentals of Data Warehouses, 2nd edn. Springer, Heidelberg (2002)
Kimball, R., Ross, M.: The Data Warehouse Toolkit, 2nd edn. John Wiley & Sons, Chichester (2002)
Luján-Mora, S., Trujillo, J.: A Data Warehouse Engineering Process. In: Yakhno, T. (ed.) ADVIS 2004. LNCS, vol. 3261, pp. 14–23. Springer, Heidelberg (2004)
Luján-Mora, S., Trujillo, J., Song, I.-Y.: Multidimensional Modeling with UML Package Diagrams. In: Spaccapietra, S., March, S.T., Kambayashi, Y. (eds.) ER 2002. LNCS, vol. 2503, p. 199. Springer, Heidelberg (2002)
Poels, G., Dedene, G.: Distance-based software measurement: necessary and sufficient properties for software measures. Information and Software Technology 42(1), 35–46 (2000)
Weyuker, E.: Evaluating Software Complexity Measures. IEEE Transactions on Software Engineering 14(9), 1357–1365 (1988)
Zuse, H.: A Framework of Software Measurement. Walter de Gruyter, Berlin (1998)
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Berenguer, G., Romero, R., Trujillo, J., Serrano, M., Piattini, M. (2005). A Set of Quality Indicators and Their Corresponding Metrics for Conceptual Models of Data Warehouses. In: Tjoa, A.M., Trujillo, J. (eds) Data Warehousing and Knowledge Discovery. DaWaK 2005. Lecture Notes in Computer Science, vol 3589. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11546849_10
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DOI: https://doi.org/10.1007/11546849_10
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