An Empirical Study of Software Metrics
IEEE Transactions on Software Engineering
C4.5: programs for machine learning
C4.5: programs for machine learning
Experimentation in software engineering: an introduction
Experimentation in software engineering: an introduction
Validation of Metrics for Object-Relational Databases
Proceedings of the Workshop on Object-Oriented Technology
Estimating Object-Relational Database Understandability Using Structural Metrics
DEXA '01 Proceedings of the 12th International Conference on Database and Expert Systems Applications
Intelligent data analysis
Selecting optimal instantiations of data models: theory and validation of an ex ante approach
Decision Support Systems
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Object-relational databases are supposed to be the substitutes of relational ones because they are a good mixture between the relational model and object-oriented principles. In this paper we present the empirical work we have developed with four metrics for object-relational databases (Percentage of Complex Columns (PCC), Number of Shared Classes (NSC), Number of Involved Classes (NIC) and Table Size (TS) defined at different granularity levels (attribute, class, table and schema). The empirical work presented is the validation made with the aim of proving the usefulness of the four metrics in estimating the complexity of an object-relational schema. This study can be considered to be a replica of another one we made with the same purpose but with two main differences: the dependent variable and the way we analyze the results. The results obtained from the empirical work seem to prove the usefulness of the TS metric in estimating the complexity of an object-relational schema however, conclusions about the other metrics are difficult to extract.