Analysis of Competing Data Structures: Does Ontological Clarity Produce Better End-User Query Performance?

End users respond to the information requests of stakeholders by using SQL-based query tools to retrieve information from their organizations’ data stores. The structure of these data stores impacts end users’ performance (e.g., the accuracy of their queries). Traditionally, logical data models with optional properties (referred to hereafter as more-traditional models) are used. Ontologically clearer conceptual models, however, have been shown to facilitate better under- standing of real-world application domains. The question now arises as to whether ontologically clearer (i.e., more precise) implementation representations also improve end-user query performance. This paper reports the results of an experiment that investigates the effect on query performance of more-traditional logical models compared to ontologically clearer logical models. End users of the ontologically clearer implementation made fewer semantic errors and were more confident in the accuracy of their queries. No statistical difference in time was observed between users of the two types of logical models