datamodeling is the sine quo non of systems development and one of the most widely researched topics in the database literature. In the past three decades, semantic datamodeling has emerged as an alternative to trad...
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datamodeling is the sine quo non of systems development and one of the most widely researched topics in the database literature. In the past three decades, semantic datamodeling has emerged as an alternative to traditional relational modeling. The majority of the research in datamodeling suggests that the use of semantic datamodels leads to better performance;however, the findings are not conclusive and are sometimes inconsistent. The discrepancies that exist in the datamodeling literature and the relatively low statistical power in the studies make meta-analysis a viable choice in analyzing and integrating the findings of these studies.
Graphic datamodels are commonly used as a fool for presentation of information structures in the design, implementation, use and maintenance of the databases that support information systems. The methods proposed for...
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Graphic datamodels are commonly used as a fool for presentation of information structures in the design, implementation, use and maintenance of the databases that support information systems. The methods proposed for database design assume that the use of graphic datamodels will enhance understanding of system specifications by both the end-users and the implementers of the system. For this assumption to hold, the information presented in the graphic datamodel must be readily comprehensible so that the design, represented by the model, can be confirmed and implemented correctly. The lack of standard representations for graphic models has led to a variety of graphic styles. To date, there has been little focus on studying the effect graphic style has on modelcomprehension. We have studied the effect of three graphic styles proposed for datamodels on model legibility and interpretation. Our study shows a significant variation in model interpretation that can be attributed to the graphic syntax used. Graphic style appears to influence which model elements are included in the interpretation, as well as the way datamodels are read.
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