Empirical study to predict the understandability of requirements schemas of data warehouse using requirements metrics. (6th January 2022)

• Record Type: Journal Article
• Authors: Singh, Tanu; Kumar, Manoj
• Is Part Of: International journal of intelligent engineering informatics. Volume 9:Number 4(2021)
• Page Start: 329
• Page End: 354
• Publication Date: 2022-01-06
• Subjects: artificial neural network -- data warehouse -- requirements engineering -- requirements metrics -- requirements schemas understandability -- Random forest; Artificial intelligence -- Engineering applications -- Periodicals; Engineering -- Computer programs -- Periodicals; Knowledge management -- Periodicals; 620.0028563
• Languages: English
• ISSNs: 1758-8715

Abstract

Information quality of data warehouse is assessed by its data model quality. Various authors have proposed metrics for data models, that are designed to capture physical, conceptual, logical and requirements views of data warehouse. These metrics were validated not only formally but also empirically to assess quality of the respective data models. However, very less work was seen in the literature to assess quality of requirements model. Therefore, in this paper, an empirical validation of requirements metrics are performed to predict the understandability of requirements schemas of data warehouse using machine learning techniques (random forest and artificial neural network). Result shows that, artificial neural network technique performed better than random forest technique. In this way, effect of requirements metrics on understandability of schemas has been assessed, thus, good quality of requirements schema may be identified and help to the designers for producing better quality of conceptual schema.