From BPMN process models to DMN decision models. (July 2019)

• Record Type: Journal Article
• Authors: Bazhenova, Ekaterina; Zerbato, Francesca; Oliboni, Barbara; Weske, Mathias
• Is Part Of: Information systems. Volume 83(2019)
• Page Start: 69
• Page End: 88
• Publication Date: 2019-07
• Subjects: Business process models -- Decision models -- BPMN -- DMN -- Pattern; Database management -- Periodicals; Electronic data processing -- Periodicals; Bases de données -- Gestion -- Périodiques; Informatique -- Périodiques; Database management; Electronic data processing; Periodicals; 005.7
• DOI: 10.1016/j.is.2019.02.001
• Languages: English
• ISSNs: 0306-4379

Abstract

Abstract: The interplay between process and decision models plays a crucial role in business process management, as decisions may be based on running processes and affect process outcomes. Often process models include decisions that are encoded through process control flow structures and data flow elements, thus reducing process model maintainability. The Decision Model and Notation (DMN) was proposed to achieve separation of concerns and to possibly complement the Business Process Model and Notation (BPMN) for designing decisions related to process models. Nevertheless, deriving decision models from process models remains challenging, especially when the same data underlie both process and decision models. In this paper, we explore how and to which extent the data modeled in BPMN processes and used for decision-making may be represented in the corresponding DMN decision models. To this end, we identify a set of patterns that capture possible representations of data in BPMN processes and that can be used to guide the derivation of decision models related to existing process models. Throughout the paper we refer to real-world healthcare processes to show the applicability of the proposed approach. Highlights: A set of BPMN patterns characterizing process-related data used for decision-making. Derive a DMN decision model from the data perspective a BPMN process model. Support decision analysts in identifying process data relevant for decision-making. Improve understanding of integrated process and decision models through shared data.