Research on user–database interaction studies the communication issues among users with various roles, such as end-user, requirement analyst, database designer, query programmer, as well as between users and databases through various database management systems. Many topics are included. Common topics include the formal languages used in database models and queries, with focus on designer and user writing effectiveness and efficiency, as well as end-user comprehension and problem solving capabilities. Research results are generally applicable to the communication processes for users, user training, and the design of database management systems, such as query interfaces and choice of database models.

Understanding data-modeling performance can provide valuable lessons for the selection, training, research, and development of data models. Data modeling is the process of transforming expressions in loose natural language communications into formal diagrammatic or tabular expressions. While researchers generally agree that abstraction levels can be used to explain general performance differences across models, empirical studies have reported many construct level results that cannot be explained. To explore further explanations, we develop a set of model-specific construct complexity values based on both theoretical and empirical support from complexity research in databases and other areas. We find that abstraction levels and complexity values together are capable of providing a consistent explanation of laboratory experiment data. In our experiment, data were drawn from three models: the relational model, the extended-entity-relationship model, and the object-oriented model. With the newly developed complexity measures, a consistent explanation can be made for findings from other studies which provide sufficient model details for complexity values to be calculated.