Conference Proceeding
Refine
Year of publication
Document Type
- Conference Proceeding (464) (remove)
Language
- English (328)
- German (134)
- Multiple languages (2)
Is part of the Bibliography
- yes (464)
Keywords
- virtual reality (5)
- Augmented Reality (2)
- Business Process Management Systems (2)
- Fuzzy Logic (2)
- Virtual Reality (2)
- visualization (2)
- 3D-printing (1)
- 3D-printing of optics (1)
- Additive Manufacturing (1)
- Assignment Automation (1)
Institute
- Wirtschaftswissenschaften (144)
- Maschinenbau und Werkstofftechnik (96)
- Elektronik und Informatik (90)
- Optik und Mechatronik (70)
- Chemie (3)
- Mechatronik (1)
A Context and Augmented Reality BPMN and BPMS Extension for Industrial Internet of Things Processes
(2022)
In the context of Industry 4.0, smart factories enable a new level of highly individualized and very efficient production, driven by highly automated processes and connected Industrial Internet of Things (IIoT) devices. Yet the IIoT process context, crucial for operational process enactment, cannot be readily represented in processes as currently modeled. Despite automation progress, manual tasks performed by humans (such as maintenance) remain, and while complicated tasks can be supported by Augmented Reality (AR) devices, they remain insufficiently integrated into global production processes. To seamlessly integrate process automation, IIoT context, and AR, this paper contributes BPMN-CARX, a Context and Augmented Reality eXtension (CARX) for BPMN (Business Process Model and Notation) and the CARX Framework, which enables AR and IIoT context integration with existing Business Process Management Systems (BPMSs). An Industry 4.0 case study demonstrates its feasibility and applicability.
As the amount of software source code increases, manual approaches for documentation or detection of software design patterns in source code become inefficient relative to the value. Furthermore, typical automatic pattern detection tools are limited to a single programming language. To address this, our Design Pattern Detection using Machine Learning (DPDML) offers a generalized and programming language agnostic approach for automated design pattern detection based on machine learning (ML). The focus of our evaluation was on ensuring DPDML can reasonably detect one design pattern in the structural, creational, and behavioral category for two popular programming languages (Java and C#). 60 unique Java and C# code projects were used to train the artificial neural network (ANN) and 15 projects were then used to test pattern detection. The results show the feasibility and potential for pursuing an ANN approach for automated design pattern detection.