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, Kunihiko Higa
Copyright: © 1990 |Pages: 17
DOI: 10.4018/jdm.1990100101
OnDemand:
(Individual Articles)
Available
$37.50
Current Special Offers
No Current Special Offers
TOTAL SAVINGS: $37.50

Abstract

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).

Complete Article List

Search this Journal:
Reset
Volume 35: 1 Issue (2024)
Volume 34: 3 Issues (2023)
Volume 33: 5 Issues (2022): 4 Released, 1 Forthcoming
Volume 32: 4 Issues (2021)
Volume 31: 4 Issues (2020)
Volume 30: 4 Issues (2019)
Volume 29: 4 Issues (2018)
Volume 28: 4 Issues (2017)
Volume 27: 4 Issues (2016)
Volume 26: 4 Issues (2015)
Volume 25: 4 Issues (2014)
Volume 24: 4 Issues (2013)
Volume 23: 4 Issues (2012)
Volume 22: 4 Issues (2011)
Volume 21: 4 Issues (2010)
Volume 20: 4 Issues (2009)
Volume 19: 4 Issues (2008)
Volume 18: 4 Issues (2007)
Volume 17: 4 Issues (2006)
Volume 16: 4 Issues (2005)
Volume 15: 4 Issues (2004)
Volume 14: 4 Issues (2003)
Volume 13: 4 Issues (2002)
Volume 12: 4 Issues (2001)
Volume 11: 4 Issues (2000)
Volume 10: 4 Issues (1999)
Volume 9: 4 Issues (1998)
Volume 8: 4 Issues (1997)
Volume 7: 4 Issues (1996)
Volume 6: 4 Issues (1995)
Volume 5: 4 Issues (1994)
Volume 4: 4 Issues (1993)
Volume 3: 4 Issues (1992)
Volume 2: 4 Issues (1991)
Volume 1: 2 Issues (1990)
View Complete Journal Contents Listing