Expressing casual relationships in conceptual database schemas
Abstract Conceptual schema design is a crucial phase in the database design process. The quality of the final database (regardless of logical implementation model) is dependent largely upon the quality of the conceptual schema. Since conceptual schemas serve as formal representations of the requirements specification for a database, it is critical that a schema capture the requirements as completely and unambiguously as possible. Many studies have shown that semantic models, such as the Extended Entity–Relationship model, are better for conceptual database design than traditional models such as relational, hierarchical, and network models. This is primarily because of their ability to capture explicitly many “natural” cognitive relationship types that are likely to occur in requirements specifications, e.g., association, generalization/specialization, and aggregation. However, the relationships that can be specified in a semantic model represent only a subset of the relationships that are likely to be used by people in describing an application 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 is represented inappropriately.This paper seeks to help bridge the gap between requirements specifications and data modeling by hypothesizing the need for supporting additional cognitive relationship types in conceptual models. In the paper, we demonstrate the need for one such relationship type, causation. 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 subjects not familiar with data modeling expressed causal relationships better in their representations than did subjects who had some prior exposure to data modeling. This seems to indicate that the lack of constructs for capturing causation in semantic models hinders the ability of people trained in 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.