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An Experimental Replication With Data Warehouse Metrics

Author

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  • Manuel Serrano

    (University of Castilla-La Mancha, Spain)

  • Coral Calero

    (University of Castilla-La Mancha, Spain)

  • Mario Piattini

    (University of Castilla-La Mancha, Spain)

Abstract

Data warehouses are large repositories that integrate data from several sources for analysis and decision support. Data warehouse quality is crucial, because a bad data warehouse design may lead to the rejection of the decision support system or may result in non-productive decisions. In the last years, we have been working on the definition and validation of software metrics in order to assure data warehouse quality. Some of the metrics are adapted directly from previous ones defined for relational databases, and others are specific for data warehouses. In this paper, we present part of the empirical work we have developed in order to know if the proposed metrics can be used as indicators of data warehouse quality. Previously, we have developed an experiment and its replication, and in this paper, we present the second replication we have made with the purpose of assessing data warehouse maintainability. As a result of the whole empirical work, we have obtained a subset of the proposed metrics that seem to be good indicators of data warehouse quality.

Suggested Citation

  • Manuel Serrano & Coral Calero & Mario Piattini, 2005. "An Experimental Replication With Data Warehouse Metrics," International Journal of Data Warehousing and Mining (IJDWM), IGI Global, vol. 1(4), pages 1-21, October.
    • Handle: RePEc:igg:jdwm00:v:1:y:2005:i:4:p:1-21
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    Cited by:

    1. Anjana Gosain & Jaspreeti Singh, 2017. "Quality metrics emphasizing dimension hierarchy sharing in multidimensional models for data warehouse: a theoretical and empirical evaluation," International Journal of System Assurance Engineering and Management, Springer;The Society for Reliability, Engineering Quality and Operations Management (SREQOM),India, and Division of Operation and Maintenance, Lulea University of Technology, Sweden, vol. 8(2), pages 1672-1688, November.
    2. Sangeeta Sabharwal & Sushama Nagpal & Gargi Aggarwal, 2017. "Empirical analysis of metrics for object oriented multidimensional model of data warehouse using unsupervised machine learning techniques," International Journal of System Assurance Engineering and Management, Springer;The Society for Reliability, Engineering Quality and Operations Management (SREQOM),India, and Division of Operation and Maintenance, Lulea University of Technology, Sweden, vol. 8(2), pages 703-715, November.

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