Enabling the Future Internet for Smart Cities

Enabling the Future Internet for Smart Cities

Image-based information visualization (or how to unify SciVis and InfoVis)

Prof. Alex Telea offered a presentation on how to unify scientific visualization (SciVis) and information visualization (InfoVis).

When: Thursday, September 28th, 2017, 2:00 PM.

Where: Auditório Jacy Monteiro, IME- USP.

Abstract: For decades, scientific visualization (SciVis) and information visualization (InfoVis) have been related, but still distinctly separated disciplines. Methods and techniques in the two areas have developed relatively separately, causing an arguably unnecessarily separation in the visualization field. Attempts for unification exist, but are largely based on heuristics, and subject to critique from both the SciVis and InfoVis angles. In this talk, we argue that this separation is not necessary, and, up to large extents, artificial. More specifically, we argue that the difference between SciVis and InfoVis is not a matter of design decisions only, but, more centrally, a matter of representing the structure of large data collections by means of smooth, continuous, encodings. We present a way to cast InfoVis along the same principles as the more classical SciVis, based on a continuous, multiscale, spatial representation of data. Putting it simply, we argue that visualizing large amounts of InfoVis data can use encoding techniques which share the same continuity and multiscale principles as most classical spatial SciVis (or image processing) methods use. In turn, we show how this is possible by means of defining appropriate similarity metrics and encoding principles for InfoVis data. This leverages a wealth of data simplification, encoding, and perception principles, since long available for SciVis data, for the richer realm of InfoVis data. We demonstrate our image-based paradigm by examples covering the visualization of relational, multidimensional, and time-dependent InfoVis data.

Mini-bio: Prof. Alex Telea has obtained his PhD in data visualization at the University of Eindhoven in 2000, where he worked next as assistant professor in the same field until 2007. Since then, he is full professor of multiscale visual analytics at the University of Groningen, the Netherlands. His research interests are at the crossroads of multiscale image and shape processing and information visualization and visual analytics. He has published over 200 papers on the above topics, and is the author of the textbook ‘Data Visualization — Principles and Practice’ (CRC Press, 2008, 2014).

Download the presentation slides.