Improving the Understandability of Declarative Process Discovery Results Using Easydeclare

Abstract

Declarative process models allow us to capture the behavior of a business process through temporal constraints on the evolution of process activities. In process mining, declarative process discovery focuses on deriving these constraints from event logs. Although the semantic aspects of declarative processes have been extensively investigated, there has been less focus on designing declarative visual notations that enhance model understanding and support analysts in solving process mining tasks. To improve the human understandability of declarative process models, in this paper, we present easyDeclare, a novel visual notation to specify declarative process models using the declare language. easyDeclare was developed with consideration of the well-established Moody's design principles. We conducted extensive user experiments to demonstrate that easyDeclare, when compared with the original graphical representation of declare, reduces the cognitive load required to interpret declare models of increasing complexity, making it a promising alternative to enhancing overall comprehension of declarative process discovery tasks.

Keywords: Understandability of Declarative Process Discovery ResultsProcess Modeling LanguagesDeclareeasyDeclare Visual NotationDesign Principles