Detection and Quantification of Flow Consistency in Business Process Models

TL;DR

This paper investigates how the visual layout, especially flow consistency, affects the understandability of business process models by identifying measurable features and proposing metrics to quantify flow consistency.

Contribution

It introduces three novel metrics for measuring flow consistency in business process models and empirically evaluates their effectiveness in predicting human perception.

Findings

One metric best predicts human perception of flow consistency.

Proposed metrics are computationally efficient and applicable to business process models.

Empirical evaluation shows significant correlation between metrics and user understanding.

Abstract

Business process models abstract complex business processes by representing them as graphical models. Their layout, solely determined by the modeler, affects their understandability. To support the construction of understandable models it would be beneficial to systematically study this effect. However, this requires a basic set of measurable key visual features, depicting the layout properties that are meaningful to the human user. The aim of this research is thus twofold. First, to empirically identify key visual features of business process models which are perceived as meaningful to the user. Second, to show how such features can be quantified into computational metrics, which are applicable to business process models. We focus on one particular feature, consistency of flow direction, and show the challenges that arise when transforming it into a precise metric. We propose three different metrics addressing these challenges, each following a different view of flow consistency. We then report the results of an empirical evaluation, which indicates which metric is more effective in predicting the human perception of this feature. Moreover, two other automatic evaluations describing the performance and the computational capabilities of our metrics are reported as well.