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Predicting quality of data warehouse using fuzzy logic

Author

Listed:
  • Anjana Gosain
  • Sangeeta Sabharwal
  • Sushama Nagpal

Abstract

Due to strategic importance of data warehouse (DW) as decision support systems, it has become crucial to guarantee that these repositories should provide quality information to the decision makers. Quality of data warehouse multidimensional model has significant effect on data warehouse quality and in turn on the information quality. Few authors have suggested metrics to assess the quality of data warehouse multidimensional models. Empirical validation using statistical techniques like correlation analysis, univariate and multivariate regression techniques, etc., indicated that these metrics are significantly related to the quality of multidimensional models for data warehouse. But these techniques are not able to model non-linear relationship between the metrics and quality of multidimensional model. In this paper, model based on fuzzy logic approach is proposed to approximate non-linear relationship between the metrics and the quality of multidimensional models. In order to empirically evaluate the effectiveness of the proposed approach, validation is done on the published data and results indicate that the proposed model is able to predict the output with significant accuracy.

Suggested Citation

  • Anjana Gosain & Sangeeta Sabharwal & Sushama Nagpal, 2012. "Predicting quality of data warehouse using fuzzy logic," International Journal of Business and Systems Research, Inderscience Enterprises Ltd, vol. 6(3), pages 255-268.
    • Handle: RePEc:ids:ijbsre:v:6:y:2012:i:3:p:255-268
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    Cited by:

    1. 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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