Panagiotis D. Ritsos

MEng PhD Essex, FHEA

Lecturer in Visualization

Visualization, Modelling and
Graphics (VMG) research group,

School of Computer Science
and Electronic Engineering,

Bangor University,
Dean Street, Bangor,
Gwynedd, UK, LL57 1UT

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Papers at IEEE VIS 2016

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We visited Baltimore, MD in October 2016 for IEEE VIS2016, the premier forum for advances in scientific and information visualization. This weeklong event convenes an international community of researchers and practitioners from academia, government, and industry to explore their shared interests in tools, techniques, and technology.

We presented the following:

J. C. Roberts, C. Headleand, and P. D. Ritsos, “Sketching Designs for Data-Visualization using the Five Design-Sheet Methodology,” in Tutorials of at the IEEE Conference on Visualization (IEEE VIS 2016), Baltimore, MD, USA, 2016. The tutorial will be useful for anyone who has to create visualization interfaces, and needs to think through different potential ways to display their data. At the end of the tutorial participants will understand techniques to help them be more structured in their ideation. They will be able to sketch interface designs using the Five Design Sheet methodology (FdS). While we know that some developers have started to use the Five Design-Sheet methodology, but this tutorial will start from the beginning and be suitable for any attendee. More information and resources are found on http://fds.design.
[Abstract]   [PDF]   [BibTex]  

J. C. Roberts, J. W. Mearman, P. D. Ritsos, H. C. Miles, A. T. Wilson, D. Perkins, J. R. Jackson, B. Tiddeman, F. Labrosse, B. Edwards, and R. Karl, “Immersive Analytics and Deep Maps – the Next Big Thing for Cultural Heritage & Archaeology,” in Visualization for Digital Humanities Workshop, IEEE Conference on Visualization (VIS), Baltimore, MD, USA, 2016. Archaeologists and cultural heritage experts explore complex multifaceted data that is often highly interconnected. We argue for new ways to interact with this data. Such data analysis provides a ‘grand challenge’ for computer science and heritage researchers, it is big Data, multi-dimensional, multi-typed, contains uncertain information, and the questions posed by researchers are often ill-defined (where it is difficult to guarantee an answer). We present two visions (Immersive Analytics, and Deep Mapping) as solutions to allow both expert users and the general public to interact and explore heritage data. We use pre-historic data as a case study, and discuss key technologies that need to develop further, to help accomplish these two visions.
[Abstract]   [PDF]   [BibTex]   [URL]  

P. W. S. Butcher, J. C. Roberts, and P. D. Ritsos, “Immersive Analytics with WebVR and Google Cardboard,” in Posters presented at the IEEE Conference on Visualization (IEEE VIS 2016), Baltimore, MD, USA, 2016. We present our initial investigation of a low-cost, web-based virtual reality platform for immersive analytics, using a Google Cardboard, with a view of extending to other similar platforms such as Samsung’s Gear VR. Our prototype uses standards-based emerging frameworks, such as WebVR and explores some the challenges faced by developers in building effective and informative immersive 3D visualizations, particularly those that attempt to resemble recent physical visualizations built in the community.
[Abstract]   [PDF]   [BibTex]   [URL]   [Video]   [Poster]  

J. C. Roberts, J. Jackson, C. Headleand, and P. D. Ritsos, “Creating Explanatory Visualizations of Algorithms for Active Learning,” in Posters presented at the IEEE Conference on Visualization (IEEE VIS 2016), Baltimore, MD, USA, 2016. Visualizations have been used to explain algorithms to learners, in order to help them understand complex processes. These ‘explanatory visualizations’ can help learners understand computer algorithms and data-structures. But most are created by an educator and merely watched by the learner. In this paper, we explain how we get learners to plan and develop their own explanatory visualizations of algorithms. By actively developing their own visualizations learners gain a deeper insight of the algorithms that they are explaining. These depictions can also help other learners understand the algorithm.
[Abstract]   [PDF]   [BibTex]   [URL]   [Video]   [Poster]  

You can find more information on the conference at IEEE VIS2016.