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DEKXTROSE: An Education 4.0 Mobile Learning Approach and Object-Aware App Based on a Knowledge Nexus
(2020)
The exponential growth in knowledge coupled with the decreasing knowledge half-life creates a challenging situation for educational programs - particularly those preparing software engineers for their very dynamic high-technology field. Teachers in high technology education areas are challenged in selecting and making relevant knowledge intuitively accessible to students, especially with regard the highly dynamic digital and software technologies. This paper contributes a knowledge nexus-based multimedia approach aligned with Higher Education 4.0 for creating learning apps on mobile devices that support multiple didactic models, leverage intrinsic curiosity and motivation, support gamification, and enable digital collaboration. Object recognition is used to trigger learning paths, and various didactic methods are supported via workflow-like learning flows to support group or team-based learning. A prototype app was realized to demonstrate its feasibility and an empirical evaluation in software engineering shows the didactic potential and advantages of the approach, which can be readily generalized and applied to the arts, sciences, etc.
Software models in the Unified Modeling Language (UML) can been created or automatically reverse-engineered and used for quickly gaining structural insights into larger, legacy, or unfamiliar software. But as the size, structural complexity, and interdependencies between software components in larger systems grows, two-dimensional viewing and modeling has limitations, and new ways of visualizing larger models and numerous associated diagrams of different types are needed to intuitively convey structural and relational insights. To investigate the feasibility of using Virtual Reality (VR) to create an immersive UML-based software modeling experience, this paper contributes a VR solution concept for visualizing, navigating, modeling, and interacting with software models using UML notation. An implementation shows its feasibility while an empirical evaluation highlights its potential.
Production processes in Industry 4.0 settings are usually highly automated. However, many complicated tasks, such as machine maintenance, must be executed by human workers. In current smart factories, such tasks can be supported by Augmented Reality (AR) devices. These AR tasks rely on high numbers of contextual factors like live data from machines or work safety conditions and are mostly not well integrated into the global production process. This can lead to various problems like suboptimal task assignment, over-exposure of workers to hazards like noise or heat, or delays in the production process. Current Business Process Management (BPM) Systems (BPMS) are not capable of readily taking such factors into account. There- fore, this contribution proposes a novel approach for context- integrated modeling and execution of processes with AR tasks. Our practical evaluations show that our AR Process Framework can be easily integrated with prevalent BPMS. Furthermore, we have created a comprehensive simulation scenario and our findings suggest that the application of this system can lead to various benefits, like better quality of AR task execution and cost savings regarding the overall Industry 4.0 processes.
Industry 4.0 production comprises complicated highly automated processes. However, human activities are also a crucial component of these processes, e.g., for machine main- tenance. Task assignment of human resources in this domain is challenging, as many factors have to be taken into account to ensure effective and efficient activity execution and satisfy special conditions (like worker safety). To overcome the limita- tions of current Business Process Management (BPM) Systems regarding activity resource assignment, this contribution provides a BPM-integrated approach that applies fuzzy sets for activity assignment. Our findings suggest that this approach can be easily applied to complex production scenarios, while providing efficient performance even with a large number of concurrent activity assignment requests. Additionally, our evaluation shows its potential for improved work distribution which can lead to cost savings in Industry 4.0 production processes.
DEKXTROSE: An Education 4.0 Mobile Learning Approach and Object-Aware App Based on a Knowledge Nexus
(2020)
The exponential growth in knowledge coupled with the decreasing knowledge half-life creates a challenging situation for educational programs - particularly those preparing software engineers for their very dynamic high-technology field. Teachers in high technology education areas are challenged in selecting and making relevant knowledge intuitively accessible to students, especially with regard the highly dynamic digital and software technologies. This paper contributes a knowledge nexus-based multimedia approach aligned with Higher Education 4.0 for creating learning apps on mobile devices that support multiple didactic models, leverage intrinsic curiosity and motivation, support gamification, and enable digital collaboration. Object recognition is used to trigger learning paths, and various didactic methods are supported via workflow-like learning flows to support group or team-based learning. A prototype app was realized to demonstrate its feasibility and an empirical evaluation in software engineering shows the didactic potential and advantages of the approach, which can be readily generalized and applied to the arts, sciences, etc.
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.
The digital transformation occurring in enterprises results in an in- creasingly dynamic and complex IT landscape that in turn impacts enterprise architecture (EA) and its artefacts. New approaches for dealing with more com- plex and dynamic models and conveying EA structural and relational insights are needed. As EA tools attempt to address these challenges, virtual reality (VR) can potentially enhance EA tool capabilities and user insight but further investigation is needed in how this can be achieved. This paper contributes a VR solution concept for visualizing, navigating, and interacting with EA tool dynamically-generated diagrams and models using the EA tool Atlas. An im- plementation shows its feasibility and a case study using EA scenarios is used to demonstrate its potential.
Nowadays, businesses with focus on consumer-products are challenged by short production cycles, high pricing pressure, and the need to deliver new features and services in a regular interval. Currently, businesses are tackling these challenges by automating their business pro- cesses, while yet trying to be flexible by introducing methods for process variability modeling. However, for larger processes and variability models, it becomes difficult to consider, maintain, and optimize all process variations in the various execution contexts. In software development, highly agile requirements are usually tackled with a flexible microservice architecture. Nonetheless, the fast-changing service landscape is often not fully reflected in the underlying business processes, leading to inefficiency and loss of profit. With this work, we extend our framework for process variability modeling with concepts of Microflows, allowing agile business process modeling and orchestration while utilizing the full flexibility of underlying microservices. In addition, we present a case study, showing how this approach is used in the context of an IoT application
VR-EA: Virtual Reality Visualization of Enterprise Architecture Models with ArchiMate and BPMN
(2019)
The digital transformation occurring throughout enterprises results in an increasingly dynamic and complex IT landscape. As the structures with which enterprise architecture (EA) deals become more digital, larger, complex, and dynamic, new approaches for modeling, documenting, and conveying EA structural and relational aspects are needed. The potential for virtual reality (VR) to address upcoming EA modeling challenges has as yet been insufficient- ly explored. This paper contributes a VR hypermodel solution concept for visu- alizing, navigating, interacting with ArchiMate and Business Process Modeling Notation (BPMN) models in VR. An implementation demonstrates its feasibil- ity and a case study is used to show its potential.