The temporal conceptual data modelling language TREND

TL;DR

This paper introduces TREND, a highly expressive temporal conceptual data modelling language, and evaluates its understandability and usability through extensive experiments, highlighting the importance of natural language explanations and training for adoption.

Contribution

The paper presents TREND, the most expressive temporal conceptual data modelling language to date, and provides empirical evidence on its usability, graphical notation, and the impact of explanatory guidance.

Findings

Natural language explanations improve model quality.

Limited impact of transition label choices on understanding.

Training and guidance are crucial for adoption.

Abstract

Temporal conceptual data modelling, as an extension to regular conceptual data modelling languages such as EER and UML class diagrams, has received intermittent attention across the decades. It is receiving renewed interest in the context of, among others, business process modelling that needs robust expressive data models to complement them. None of the proposed temporal conceptual data modelling languages have been tested on understandability and usability by modellers, however, nor is it clear which temporal constraints would be used by modellers or whether the ones included are the relevant temporal constraints. We therefore sought to investigate temporal representations in temporal conceptual data modelling languages, design a, to date, most expressive language, TREND, through small-scale qualitative experiments, and finalise the graphical notation and modelling and understanding in large scale experiments. This involved a series of 11 experiments with over a thousand participants in total, having created 246 temporal conceptual data models. Key outcomes are that choice of label for transition constraints had limited impact, as did extending explanations of the modelling language, but expressing what needs to be modelled in controlled natural language did improve model quality. The experiments also indicate that more training may be needed, in particular guidance for domain experts, to achieve adoption of temporal conceptual data modelling by the community.