Empirical Validation of Metrics for Conceptual Models of Data Warehouses

  • Manuel Serrano
  • Coral Calero
  • Juan Trujillo
  • Sergio Luján-Mora
  • Mario Piattini
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3084)


Data warehouses (DW), based on the multidimensional modeling, provide companies with huge historical information for the decision making process. As these DW’s are crucial for companies in making decisions, their quality is absolutely critical. One of the main issues that influences their quality lays on the models (conceptual, logical and physical) we use to design them. In the last years, there have been several approaches to design DW’s from the conceptual, logical and physical perspectives. However, 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 DW’s. In this paper, we present a set of metrics to measure the quality of conceptual models for DW’s. We have validated them through an empirical experiment performed by expert designers in DW’s. Our experiment showed us that several of the proposed metrics seems to be practical indicators of the quality of conceptual models for DW’s.


Data warehouse quality data warehouse metrics 


  1. 1.
    Abelló, A., Samos, J., Saltor, F.: YAM2 (Yet Another Multidimensional Model): An extension of UML. In: International Database Engineering & Applications Symposium (IDEAS0́2), July, pp. 172–181 (2002)Google Scholar
  2. 2.
    Basili, V., Weiss, D.: A Methodology for Collecting Valid Software Engineering Data. IEEE Transactions on Software Engineering 10, 728–738 (1984)CrossRefGoogle Scholar
  3. 3.
    Briand, L., El Emam, K., Morasca, S.: Theoretical and empirical validation of software product measures. Technical Report ISERN-95-03, International Software Engineering Research Network (1995)Google Scholar
  4. 4.
    Briand, L., Wüst, J., Lounis, H.: A Comprehensive Investigation of Quality Factors in Object- Oriented Designs: an Industrial Case Study. In: 21st Intĺ Conf. Software Engineering, Los Angeles, pp. 345–354 (1999)Google Scholar
  5. 5.
    Cabbibo, L., Torlone, R.: A logical approach to multidimensional databases. In: Schek, H.-J., Saltor, F., Ramos, I., Alonso, G. (eds.) EDBT 1998. LNCS, vol. 1377, pp. 183–197. Springer, Heidelberg (1998)CrossRefGoogle Scholar
  6. 6.
    Calero, C., Piattini, M., Pascual, C., Serrano, M.A.: Towards Data Warehouse Quality Metrics. In: Workshop on Design and Management of Data Warehouses, DMDW 2001 (2001)Google Scholar
  7. 7.
    Cavero, J.M., Piattini, M., Marcos, E., Sánchez, A.: A Methodology for Data warehouse Design: Conceptual Modeling. In: 12th International Conference of the Information Resources Management Association (IRMA 2001), Toronto, Ontario, Canada (2001)Google Scholar
  8. 8.
    English, L.: Information Quality Improvement: Principles, Methods and Management, Seminar, 5th edn. Information Impact International, Inc., Brentwood (1996)Google Scholar
  9. 9.
    Fenton, N., Pfleeger, S.: Software Metrics: A Rigorous Approach, 2nd edn. Chapman & Hall, London (1997)Google Scholar
  10. 10.
    Genero, M., Olivas, J., Piattini, M., Romero, F.: Using metrics to predict OO information systems maintainability. In: Dittrich, K.R., Geppert, A., Norrie, M.C. (eds.) CAiSE 2001. LNCS, vol. 2068, pp. 388–401. Springer, Heidelberg (2001)CrossRefGoogle Scholar
  11. 11.
    Golfarelli, M., Maio, D., Rizzi, S.: The Dimensional Fact Model: A Conceptual Model for Data Warehouses. International Journal of Cooperative Information Systems (IJCIS) 7(2-3), 215–247 (1998)CrossRefGoogle Scholar
  12. 12.
    Inmon, W.H.: Building the Data Warehouse, 3rd edn. John Wiley and Sons, USA (2003)Google Scholar
  13. 13.
    ISO International Standard ISO/IEC 9126. Information technology – Software product evaluation. ISO, Geneve (2001)Google Scholar
  14. 14.
    Jarke, M., Lenzerini, M., Vassiliou, Y., Vassiliadis, P.: Fundamentals of Data Warehouses, Ed. Springer, Heidelberg (2000)zbMATHGoogle Scholar
  15. 15.
    Kimball, R., Ross, M.: The Data Warehouse Toolkit, 2nd edn. John Wiley & Sons, Chichester (2002)Google Scholar
  16. 16.
    Kitchenham, B., Pflegger, S., Pickard, L., Jones, P., Hoaglin, D., El-Emam, K., Rosenberg, J.: Preliminary Guidelines for Empirical Research in Software Engineering. IEEE Transactions of Software Engineering 28(8), 721–734 (2002)CrossRefGoogle Scholar
  17. 17.
    Luján-Mora, S., Trujillo, J., Song, I.-Y.: Extending UML for Multidimensional Modeling. In: Jézéquel, J.-M., Hussmann, H., Cook, S. (eds.) UML 2002. LNCS, vol. 2460, pp. 290–304. Springer, Heidelberg (2002)Google Scholar
  18. 18.
    Poels, G., Dedene, G.: Distance-based Software Measurement: Necessary and Sufficient Properties for Software Measures. Information and Software Technology 42(1), 35–46 (2000)CrossRefGoogle Scholar
  19. 19.
    Sapia, C., Blaschka, M., Höfling, G., Dinter, B.: Extending the E/R Model for the Multidimensional Paradigm. In: Kambayashi, Y., Lee, D.-L., Lim, E.-p., Mohania, M., Masunaga, Y. (eds.) ER Workshops 1998. LNCS, vol. 1552, pp. 105–116. Springer, Heidelberg (1999)CrossRefGoogle Scholar
  20. 20.
    Serrano, M., Calero, C., Piattini, M.: Validating metrics for data warehouses. IEE Proceedings SOFTWARE 149(5), 161–166 (2002)CrossRefGoogle Scholar
  21. 21.
    Serrano, M., Calero, C., Piattini, M.: Experimental validation of multidimensional data models metrics. In: Proc of the Hawaii International Conference on System Sciences (HICSS 36), IEEE Computer Society, Los Alamitos (2003)Google Scholar
  22. 22.
    Si-Saïd, S., Prat, N.: Multidimensional Schemas Quality: Assessing and Balancing Analyzability and Simplicity. In: Jeusfeld, M.A., Pastor, Ó. (eds.) ER Workshops 2003. LNCS, vol. 2814, pp. 140–151. Springer, Heidelberg (2003)CrossRefGoogle Scholar
  23. 23.
    Tryfona, N., Busborg, F., Christiansen, G.B.: starER: A Conceptual Model for Data Warehouse Design. In: Proceedings of the ACM Second International Workshop on Data Warehousing and OLAP (DOLAP 1999), Kansas City, USA, pp. 3–8 (1999)Google Scholar
  24. 24.
    Trujillo, J., Palomar, M., Gómez, J., Song, I.-Y.: Designing Data Warehouses with OO Conceptual Models. IEEE Computer, Special issue on Data Warehouses 34(12), 66–75 (2001)Google Scholar
  25. 25.
    Vassiliadis, P.: Data Warehouse Modeling and Quality Issues. Ph.D. Thesis. National Technical University of Athens (2000)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2004

Authors and Affiliations

  • Manuel Serrano
    • 1
  • Coral Calero
    • 1
  • Juan Trujillo
    • 2
  • Sergio Luján-Mora
    • 2
  • Mario Piattini
    • 1
  1. 1.Alarcos Research Group, Escuela Superior de InformáticaUniversity of CastillaCiudad Real
  2. 2.Dept. de Lenguajes y Sistemas InformáticosUniversidad de Alicante

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