Nov 30, 2018 | 15:00—16:00
Data analysis and data mining in particular as emerging fields of data science have gained wide popularity in the last years both in academia and in industry. As an example, recent research initiatives such as Industry 4.0, SmartFactory or Internet-of-Things (IoT) try to motivate researchers and engineers to improve the production and products in various application fields by utilizing technologies for data analysis such as clustering, filtering and visual data analysis for example. Beside many issues that have to be solved in this intent, the major problem that still remains here is how to deal with the growing amount of data/information, i.e., a so-called information overload problem. Visual data analysis has been proven to be one of the effective ways to tackle this issue. Visualizations have a distinctive advantage when dealing with the information overload problem: because they are grounded in basic visual cognition, many people understand them and can naturally perform visual operations such as clustering, filtering and comparing quantities. However, creating appropriate visualizations requires specific expertise of the domain and underlying data. Yet, an ordinary user lacks expert knowledge and can rarely generate sophisticated visualizations. Thus, the first quest of this work is to provide strategies to automatically recommend appropriate visualizations for non-experts by following visual encoding rules and perceptual guidelines. Yet, considering just visual encoding rules leads to a large set of possibilities, valid in terms of representing the data visually, but without considering which type serves the users’ needs best. To tackle this issue, we propose a novel recommender system that (i) recommends visualizations based on a set of visual cognition rules and (ii) filters a subset considering the user’s preferences. This research work investigates different strategies to recommend visualizations considering different aspects of the user preferences/needs/interest.
Belgin Mutlu is a senior researcher at the Know Center GmbH and area manager at the Pro2Future GmbH in Graz. She received her Master’s and Bachelor’s degree in Telematics, and her PhD degree in Informatics from Graz University of Technology. Her research interests include visualizations, visual data analysis, recommender systems and semantic web. Belgin has worked as researcher and developer in the field of adaptive visualizations, linked data and visual recommender systems in several EU Projects—CODE, EEXCESS, and AFEL. She has co-authored more than 15 peer-reviewed publications.