Complexity metric for multidimensional models for data warehouse

  • Authors:

  • Sushama Nagpal;Anjana Gosain;Sangeeta Sabharwal

  • Affiliations:

  • Netaji Subhas Institute of Technology, Dwarka, New Delhi, India;School of Information Technology, Dwarka, New Delhi, India;Netaji Subhas Institute of Technology, Dwarka, New Delhi, India

  • Venue:
  • Proceedings of the CUBE International Information Technology Conference
  • Year:
  • 2012

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Abstract

Quality of data models for data warehouse has significant effect on the quality of data warehouse. Complexity metrics play significant role in predicting quality attributes of a software artifact. Few researchers have proposed structural complexity metrics for the multidimensional data models for data warehouse which may act as objective indicators of the quality of these models. However, the metrics proposed earlier have not considered the structural complexity due to relationships among various elements present in these models. This paper proposes a complexity metric which considers structural complexity due to relationships among elements present in multidimensional models for data warehouse. The metric is proposed on the basis of Goal Question Metric approach. The practical usefulness of the proposed metric is proved by validating the metric using a practical framework proposed by Kaner. This preliminary validation suggests that the metric may be linked to the quality of the multidimensional models. The advantage of the metric is that it is available during early phase of software development life cycle. The metric will also help the developers to select quality data model among various semantically equivalent models.