Expressing casual relationships in conceptual database schemas

Conceptual schema design is a crucial phase in the database design process. The quality of the final database (regardless of logical implementation mo del) is dependent largely upon the quality of the conceptual schema. Since conceptual schemas serve as formal representations of the requirements spec ification for a database, it is critical that a schema capture the requirem ents as completely and unambiguously as possible. Many studies have shown t hat semantic models, such as the Extended Entity-Relationship model, are be tter for conceptual database design than traditional models such as relatio nal, hierarchical, and network models. This is primarily because of their a bility to capture explicitly many "natural" cognitive relationship types th at are likely to occur in requirements specifications, e.g., association, g eneralization/specialization, and aggregation. However, the relationships t hat can be specified in a semantic model represent only a subset of the rel ationships that are likely to be used by people in describing an applicatio n environment. Thus, using current semantic models for conceptual database design may result in abstractions of application environments in which some important information from the requirements is either not represented or i s represented inappropriately. This paper seeks to help bridge the gap betw een requirements specifications and data modeling by hypothesizing the need for supporting additional cognitive relationship types in conceptual model s. In the paper, we demonstrate the need for one such relationship type, ca usation. Specifically, we investigate the effects of the lack of constructs in semantic models for capturing causation on analysts' ability to express causal relationships mentioned in a requirements document. We found that s ubjects not familiar with data modeling expressed causal relationships bett er in their representations than did subjects who had some prior exposure t o data modeling. This seems to indicate that the lack of constructs for cap turing causation in semantic models hinders the ability of people trained i n data modeling techniques to recognize and express causal relationships in conceptual schemas. The results also suggest the need to develop semantic models that provide constructs for capturing causation and other cognitive relationships. (c) 1999 Elsevier Science Inc. All rights reserved.