Interactive Gpu Based Visualization Of Large Dynamic Particle Data
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Interactive GPU-based Visualization of Large Dynamic Particle Data
Prevalent types of data in scientific visualization are volumetric data, vector field data, and particle-based data. Particle data typically originates from measurements and simulations in various fields, such as life sciences or physics. The particles are often visualized directly, that is, by simple representants like spheres. Interactive rendering facilitates the exploration and visual analysis of the data. With increasing data set sizes in terms of particle numbers, interactive high-quality visualization is a challenging task. This is especially true for dynamic data or abstract representations that are based on the raw particle data. This book covers direct particle visualization using simple glyphs as well as abstractions that are application-driven such as clustering and aggregation. It targets visualization researchers and developers who are interested in visualization techniques for large, dynamic particle-based data. Its explanations focus on GPU-accelerated algorithms for high-performance rendering and data processing that run in real-time on modern desktop hardware. Consequently, the implementation of said algorithms and the required data structures to make use of the capabilities of modern graphics APIs are discussed in detail. Furthermore, it covers GPU-accelerated methods for the generation of application-dependent abstract representations. This includes various representations commonly used in application areas such as structural biology, systems biology, thermodynamics, and astrophysics.
Diversity in Visualization
At the 2016 IEEE VIS Conference in Baltimore, Maryland, a panel of experts from the Scientific Visualization (SciVis) community gathered to discuss why the SciVis component of the conference had been shrinking significantly for over a decade. As the panelists concluded and opened the session to questions from the audience, Annie Preston, a Ph.D. student at the University of California, Davis, asked whether the panelists thought diversity or, more specifically, the lack of diversity was a factor. This comment ignited a lively discussion of diversity: not only its impact on Scientific Visualization, but also its role in the visualization community at large. The goal of this book is to expand and organize the conversation. In particular, this book seeks to frame the diversity and inclusion topic within the Visualization community, illuminate the issues, and serve as a starting point to address how to make this community more diverse and inclusive. This book acknowledges that diversity is a broad topic with many possible meanings. Expanded definitions of diversity that are relevant to the Visualization community and to computing at large are considered. The broader conversation of inclusion and diversity is framed within the broader sociological context in which it must be considered. Solutions to recruit and retain a diverse research community and strategies for supporting inclusion efforts are presented. Additionally, community members present short stories detailing their ""non-inclusive"" experiences in an effort to facilitate a community-wide conversation surrounding very difficult situations. It is important to note that this is by no means intended to be a comprehensive, authoritative statement on the topic. Rather, this book is intended to open the conversation and begin to build a framework for diversity and inclusion in this specific research community. While intended for the Visualization community, ideally, this book will provide guidance for any computing community struggling with similar issues and looking for solutions.
Adaptive and Personalized Visualization
There is ample evidence in the visualization community that individual differences matter. These prior works highlight various personality traits and cognitive abilities that can modulate the use of the visualization systems and demonstrate a measurable influence on speed, accuracy, process, and attention. Perhaps the most important implication of this body of work is that we can use individual differences as a mechanism for estimating when a design is effective or to identify when people may struggle with visualization designs. These effects can have a critical impact on consequential decision-making processes. One study that appears in this book investigated the impact of visualization on medical decision-making showed that visual aides tended to be most beneficial for people with high spatial ability, a metric that measures a person’s ability to represent and manipulate two- or three-dimensional representations of objects mentally. The results showed that participants with low spatial ability had difficulty interpreting and analyzing the underlying medical data when they use visual aids. Overall, approximately 50% of the studied population were unsupported by the visualization tools when making a potentially life-critical decision. As data fluency continues to become an essential skill for our everyday lives, we must embrace the growing need to understand the factors that may render our tools ineffective and identify concrete steps for improvement. This book presents my current understanding of how individual differences in personality interact with visualization use and draws from recent research in the Visualization, Human-Computer Interaction, and Psychology communities. We focus on the specific designs and tasks for which there is concrete evidence of performance divergence due to personality. Additionally, we highlight an exciting research agenda thatis centered around creating tailored visualization systems that are aligned with people’s abilities. The purpose of this book is to underscore the need to consider individual differences when designing and evaluating visualization systems and to call attention to this critical research direction.