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Hasitschka Peter, Sabol Vedran, Thalmann Stefan

Toward a Visual Analytics Framework for Learning Communities in Industry 4.0

9. Konferenz Professionelles Wissensmanagement (Professional Knowledge Management), York Sure-Vetter, Stefan Zander, Andreas Harth, Karlsruhe, Deutschland, 2017

Industry 4.0 describes the digitization and the interlinking of companies working together in a supply chain [1]. Thereby, the digitization and the interlinking does not only affects the machines and IT infrastructure, rather also the employees are affected [3]. The employees have to acquire more and more complex knowledge within a shorter period of time. To cope with this challenge, the learning needs to be integrated into the daily work practices, while the learning communities should map the organizational production networks [2]. Such learning networks support the knowledge exchange and joint problem solving together with all involved parties [4]. However, in such communities not all involved actors are known and hence support to find the right learning material and peers is needed. Nowadays, many different learning environments are used in the industry. Their complexity makes it hard to understand whether the system provides an optimal learning environment. The large number of learning resources, learners and their activities makes it hard to identify potential problems inside a learning environment. Since the human visual system provides enormous power for discovering patterns from data displayed using a suitable visual representation [5], visualizing such a learning environment could provide deeper insights into its structure and activities of the learners. Our goal is to provide a visual framework supporting the analysis of communities that arise in a learning environment. Such analysis may lead to discovery of information that helps to improve the learning environment and the users’ learning success.

Tschinkel Gerwald, Hasitschka Peter, Sabol Vedran, Hafner R

Using Micro-Visualisations to Support Faceted Filtering of Recommender Results

Information Visualisation (IV), 2016 20th International Conference, IEEE, Lisbon, Portugal, 2016

Faceted search is a well known and broadly imple- mented paradigm for filtering information with various types of structured information. In this paper we introduce a multiple-view faceted interface, consisting of one main visualisation for exploring the data and multiple minia- turised visualisations showing the filters. The Recommen- dation Dashboard tool provides several interactive visual- isations for analysing recommender results along various faceted dimensions specific to cultural heritage and scien- tific content. As our aim is to reduce the user load and opti- mise the use of screen area, we permit only one main visu- alisation to be visible at a time, and introduce the concept of micro-visualisations – small, simplified views conveying only the necessary information – to provide natural, easy to understand representation of the the active filter set.
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