Comparing BPMN to BPMN + DMN for IoT process modelling: a case-based inquiry

The network of interconnected devices that compose the Internet of Things (IoT) continues to expand. Business processes are starting to take advantage of IoT by adapting to the physical environment or by automating process tasks. The Business Process Model and Notation (BPMN) specification has been employed in numerous studies to include IoT devices and resources. While aggregating low-level IoT data into process-relevant data is of paramount importance for IoT processes, BPMN may not be the best approach to model this data aggregation. Decision Model and Notation (DMN), however, is a recently introduced standard which is inherently used to aggregate low-level information into high-level information. This makes DMN a promising match for modelling context data aggregation in IoT processes. Therefore, this paper examines the modelling of IoT processes by comparing the standard BPMN approach and the combination of BPMN and DMN. Three cases with increasing need for context aggregation are modelled according to both techniques, leading to an analysis of the capability of the approaches to support IoT processes in terms of high-level context-awareness, scalability and complexity, flexibility, and decision logic reusability. We demonstrate that in cases where a need for complex context aggregation decision logic is present, the combination of BPMN and DMN provides the required support, even for the complex cases, and performs better than BPMN on its own.

[1]  Jan Vanthienen,et al.  A Case-Based Inquiry into the Decision Model and Notation (DMN) and the Knowledge Base (KB) Paradigm , 2018, RuleML+RR.

[2]  Mark von Rosing,et al.  Business Process Model and Notation - BPMN , 2015, The Complete Business Process Handbook, Vol. I.

[3]  Gonzalo Mateos,et al.  Health Monitoring and Management Using Internet-of-Things (IoT) Sensing with Cloud-Based Processing: Opportunities and Challenges , 2015, 2015 IEEE International Conference on Services Computing.

[4]  Feng Tian An information System for Food Safety Monitoring in Supply Chains based on HACCP, Blockchain and Internet of Things , 2018 .

[5]  Jan Mendling,et al.  Declarative versus Imperative Process Modeling Languages: The Issue of Understandability , 2009, BMMDS/EMMSAD.

[6]  Diego Calvanese,et al.  Semantics, Analysis and Simplification of DMN Decision Tables , 2018, Inf. Syst..

[7]  Fabio Casati,et al.  Process-Based Design and Integration of Wireless Sensor Network Applications , 2012, BPM.

[8]  Daniel L. Moody,et al.  The “Physics” of Notations: Toward a Scientific Basis for Constructing Visual Notations in Software Engineering , 2009, IEEE Transactions on Software Engineering.

[9]  Mathias Weske,et al.  Achieving Business Process Improvement via Ubiquitous Decision-Aware Business Processes , 2019, ACM Trans. Internet Techn..

[10]  Estefanía Serral Asensio,et al.  Executing IoT Processes in BPMN 2.0: Current Support and Remaining Challenges , 2019, 2019 13th International Conference on Research Challenges in Information Science (RCIS).

[11]  Jan Vanthienen,et al.  Complexity metrics for DMN decision models , 2019, Comput. Stand. Interfaces.

[12]  Ilker Etikan,et al.  Comparison of Convenience Sampling and Purposive Sampling , 2016 .

[13]  Remco M. Dijkman,et al.  Business Process Model and Notation - Third International Workshop, BPMN 2011, Lucerne, Switzerland, November 21-22, 2011. Proceedings , 2011, Business Process Modeling Notation.

[14]  Krzysztof Kluza,et al.  Measuring Complexity of Business Process Models Integrated with Rules , 2015, ICAISC.

[15]  Jan Vanthienen,et al.  From decision knowledge to e-government expert systems: the case of income taxation for foreign artists in Belgium , 2019, Knowledge and Information Systems.

[16]  Vicente Pelechano,et al.  Addressing the evolution of automated user behaviour patterns by runtime model interpretation , 2015, Software & Systems Modeling.

[17]  Ming-Whei Feng,et al.  Complex event processing for the Internet of Things and its applications , 2014, 2014 IEEE International Conference on Automation Science and Engineering (CASE).

[18]  Andrea Giglio,et al.  A BPMN extension for modeling Cyber-Physical-Production-Systems in the context of Industry 4.0 , 2017, 2017 IEEE 14th International Conference on Networking, Sensing and Control (ICNSC).

[19]  Monique Snoeck,et al.  Pragmatic guidelines for business process modeling , 2014 .

[20]  Johannes De Smedt,et al.  Developing a Modelling and Mining Framework for Integrated Processes and Decisions , 2017, OTM Workshops.

[21]  Mathias Weske,et al.  From BPMN process models to DMN decision models , 2019, Inf. Syst..

[22]  Johannes De Smedt,et al.  Redesigning Processes for Decision-Awareness: Strategies for Integrated Modelling , 2018, 2018 11th International Conference on the Quality of Information and Communications Technology (QUATIC).

[23]  Johannes De Smedt,et al.  Augmenting processes with decision intelligence: Principles for integrated modelling , 2018, Decis. Support Syst..

[24]  Agnes Koschmider,et al.  On the Contextualization of Event-Activity Mappings , 2018, Business Process Management Workshops.

[25]  Barry W. Boehm,et al.  Cost models for future software life cycle processes: COCOMO 2.0 , 1995, Ann. Softw. Eng..

[26]  Carlo Combi,et al.  Seamless Design of Decision-Intensive Care Pathways , 2016, 2016 IEEE International Conference on Healthcare Informatics (ICHI).

[27]  Johannes De Smedt,et al.  Holistic discovery of decision models from process execution data , 2019, Knowl. Based Syst..