Navigating the Space of Visualizations

Speaker:  Stefan Bruckner – Bergen, Norway
Topic(s):  Graphics and Computer-Aided Design

Abstract

Considering the vast amounts of data involved in many scientific disciplines and industrial applications, it is essential to provide effective and efficient means for forming a mental model of the underlying phenomenon. The term "visualization" refers to the process of extracting meaningful information from data and constructing a visual representation of this information.

While the concept of using images to communicate complex phenomena of course predates the development of digital technology by millennia, over the past decades the field of visualization has firmly established itself as an important and constantly expanding discipline within computer science. Computer-based visualization seeks to provide interactive graphical data representations, taking advantage of the extraordinary capability of the human brain to process visual information. Advanced visualization methods now play an important role in the exploration, analysis, and presentation of data in many fields such as medicine, biology, geology, or engineering. This development, however, has also lead to the fact that there is now a vast number of often very specialized techniques to visualize different types of data tailored towards specific tasks. Hence, particularly for non-experts, it becomes increasingly difficult to choose appropriate methods that will provide the optimal answers to their questions.

In this talk, I will discuss previous and ongoing research on how we can explore and navigate the space of visualizations itself. By consider the interplay between data, visualization algorithms, their parameters, perception, and cognition as a complex phenomenon that deserves study in its own right, we are making progress in providing goal-oriented interfaces for visual analysis. For instance, we can make the modification of input parameters of visualization algorithms more intuitive by normalizing their perceived effects over the entire value range, and provide visual guidance about their influence. Furthermore, by incorporating additional knowledge into the visualization process, we can infer information about the goals of a user, and develop smarter systems that automatically suggest appropriate visualization techniques. This line of investigation leads us along the path towards a new type of visual data science, where automated data analysis approaches such as deep learning are tightly coupled with interactive visualization techniques to exploit their complementary advantages for knowledge discovery in data-driven science.

About this Lecture

Number of Slides:  102
Duration:  45 - 60 minutes
Languages Available:  English
Last Updated: 

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