Empirical evidence for the usefulness of Armstrong relations in the acquisition of meaningful functional dependencies

Armstrong relations satisfy precisely those data dependencies that are implied by a given set of data dependencies. A common perception is that Armstrong relations are useful in the acquisition of data semantics, in particular since errors during the requirements elicitation have the most expensive consequences.We report on some first empirical evidence for this perception regarding the class of functional dependencies (FDs). For this purpose, we investigate the usefulness of Armstrong relations with respect to various measures. Soundness measures how many of the as meaningful perceived FDs are actually meaningful. Completeness measures how many of the actually meaningful FDs are also perceived as meaningful.Our experiment determines what and how much design teams learn about the application domain in addition to what they know prior to using Armstrong relations. The data analysis suggests that in using Armstrong relations it is not more likely to recognize meaningless FDs which are incorrectly perceived as meaningful, but it is more likely to recognize meaningful FDs that are incorrectly perceived as meaningless.Our measures assess the quality of an FD set with respect to a target FD set, and therefore qualify naturally for the use in automated assessment tools, e.g. for database course exams or assignments.