An Analytical and Empirical Comparison of End-User Logical Database Design Methods

An Analytical and Empirical Comparison of End-User Logical Database Design Methods

Olivia R. Sheng (University of Arizona, USA) and Kunihiko Higa (Georgia Institute of Technology, USA)
Copyright: © 1990 |Pages: 17
DOI: 10.4018/jdm.1990100101
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The current trend toward increasing database applications and end-user computing brings about the need for effective training and support tools that enable end users to perform logical database designs. This paper presents a unique comparison of a network-based data model (the extended entity relationship (EER)) and a hierarchical-based data model (the structured object model (SOM)) and their associated logical database design methods, LRDM (EER-based) and the SOM method, in end-user logical database design. The SOM method, based on an object-oriented knowledge representation scheme, is a new structured method designed to be less susceptible to designer errors and yet capable of maintaining ease of understanding and use. The Logical Relational Design Methodology (LRDM), based on the EER model, shares some common characteristics with the SOM method. In the analytic comparison the basic components and constructs of EER and SOM, as well as their use in the logical design process, are compared to derive a qualitative assessment of the two methods. Two experiments using novice database designers were conducted to examine the effectiveness of each method in leading to accurate logical designs, the time needed for task completion and the improvement in performance over a short training period. To confirm the findings from other studies on logical database design using non-graphic methods versus using graphic methods, the normalization method was included in one of the experiments. The results showed that end users performed better with graphic methods and also preferred them to the non-graphic (normalization) method. Furthermore, users learned and improved in a short period using LRDM (EER-based). However, users of the SOM method generated more accurate designs, were able to identify more complex relationships, and had more significant improvements during the training period than did users of LRDM (EER-based).

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