Comparing a rule-based approach with a pattern-based approach at different levels of complexity of conceptual data modelling tasks

It is well known that conceptual database design is an unusually difficult and error-prone task for novice designers. To address the problem, at least two training approaches-rule-based and pattern-based-have been suggested. A rule-based approach prescribes a sequence in modelling the conceptual modelling constructs, and the action to be taken at each stage. A pattern-based approach presents data modelling structures that occur frequently in practice, and prescribes guidelines on how to recognize these structures. This paper describes the conceptual framework, experimental design, and results of a laboratory study that employed novice designers to compare the effectiveness of the two training approaches (between-subjects) at three levels of task complexity (within subjects). Results indicate an interaction effect between treatment and task complexity. The rule-based approach was significantly better in the low-complexity and the high-complexity cases; there was no statistical difference in the medium-complexity case. Designer performance fell significantly as complexity increased. Overall, although the rule-based approach was not significantly superior to the pattern-based approach, the study still recommends the rule-based approach for novice designers given the significantly better performance at two out of three complexity levels. (C) 2004 Elsevier Ltd. All rights reserved.