Context-aware systems in intelligent environments digest large amounts of data and personal information to gain situational awareness as a way to assist individuals with their daily activities, enhance their experiences and adapt to their needs and intention, whenever and wherever they are. Large amounts of data drive these environments, motivating the adoption of big data and cloud technologies. A similar digital transformation is taking place in the Factory of the Future and Industry 4.0, two paradigms on creating smart products through smart processes and procedures.
Typical learning factories are characterized by selective simplification or scaling-down of complex and large-scale production processes, while also safely containing risks in the case of process failures inherent to experimental and didactic activities. The variety of aspects preserved by these scaled-down environments allow different approaches to be taken in research and education. The paper compares two facilities, at TU Wien and at MTA SZTAKI in Budapest, respectively, and highlights differences in their modes of operation, the resulting variations of course-based vs.
Short description: Digitalization causes a radical transformation of industries, providing unique opportunities for value creation and competitive advantage. Software-defined networking (SDN), often combined with network function virtualization (NFV), is now reshaping the datacenter and telecom landscape. The complexity of new networking technologies is often perceived as a threat, but SDN offers an opportunity to increase efficiency, to create innovative offerings for customers and to (re-)conquer the market.
Short description: Attendance at a workshop of "Netzwerk Innovativer Lernfabriken". During the workshop, the EXCELL project was presented to attending network members, and a targeted consultation took place with experts building and operating the learning factory facility at Reutlingen. The discussion focused on representation of manufacturing aspects related to massive process transparency and (remote) decision support in the dominantly manual and small-scale work processes modeled in the learning factory.
The Internet of Things (IoT) is a growing field which is expected to generate and collect data everywhere at any time. Highly scalable cloud analytics systems are frequently being used to handle this data explosion. However, the ubiquitous nature of the IoT data imposes new technical and non-technical requirements which are difficult to address with a cloud deployment. To solve these problems, we need a new set of development technologies such as Distributed Data Mining and Ubiquitous Data Mining targeted and optimized towards IoT applications.