Visualization enables new forms of social expression beyond the support of scientific data analysis. Focusing on the expanded roles of computational visualization, I investigate the influences of computation on the aesthetics and the rhetoric of visualization through design research methods. My design research includes 1) the construction of knowledge by synthesizing literature from digital media studies, visual rhetoric, information visualization, graphic design history, and HCI and 2) research through practices and consequent critiques. Coded visualization is a new term that I coined to integrate the rhetoric and aesthetics of data visualization. I define it as a data-based interface whose visual form is an aesthetic space where messages are coded and interpreted with cultural references. I also suggest the design criteria of coded visualization, apply them to a design project, and critique how the current design of the project can be improved to fully exemplify the concept of coded visualization. This study on the rhetoric and aesthetics of visualization through design research contributes to digital media studies, design research, as well as information visualization.

It is important for the military to know as much as possible about how easily detected their vehicles are. One way among many used to detect vehicles is the use of radar sensors. The radar reflecting characteristics of military vehicles are therefor often rigorously tested. With measurements and simulations it is possible to calculate likely detection distances to a vehicle from different angles. This process often produces very large data sets that are hard to analyze. This thesis discusses and implements a method for visualizing the detection distance data set and also discusses a lot of related issues with a focus on computer graphics. The main concept is called spherical displacement and the idea is to visualize the detection distances as a surface with the imagined vehicle in the center point. Detection is likely inside the surface but not on the outside. This concept is the next step from the colored sphere where the colors represent the detection distance which was previously used. The thesis project resulted in a visualization tool that uses the new concept and can handle large data sets. The spherical displacement concept is more intuitive and shows detail better than the colored sphere visualization.

The purpose of this study was to create a new method for visualizing music. Although many music visualizations already exist, this research was focused on creating high-quality, high-complexity animations that cannot be matched by real-time systems. There should be an obvious similarity between the input music and the final animation, based on the music information which the user decides to extract and visualize. This project includes a pipeline for music data extraction and creation of an editable visualization file. Within the pipeline, a music file is read into a custom analysis tool and time-based data is extracted. This data is output and then read into Autodesk Maya. The user may then manipulate the visualization as they see fit using the tools within Maya and render out a final animation. The default result of this process is a Maya scene file which makes use of the dynamics systems available to warp and contort a jelly-like cube. A variety of other visualizations may be obtained by mapping the data to different object attributes within the Maya interface. When rendered out and overlaid onto the music, there was a recognizable correlation between elements in the music and the animations in the video. This study shows that an accurate musical visualization may be achieved using this pipeline. Also, any number of different music visualizations may be obtained with relative ease when compared to the manual analysis of a music file or the manual animation of Maya objects to match elements in the music.

Thesis (Ph. D.)--Computing, Georgia Institute of Technology, 2006. / Includes bibliographical references (p. 160-171).

In master’s diploma work „The investigation of multidimensional data visualization methods“ the wide review of multidimensional data visualization methods is presented. The author was limited to research only two multidimensional data visualization methods such as: a parallel coordinates visualization method and Andrews curves visualization method. The program realization of both methods is realised, i. e. computer program was written for comparing those methods. To write program the tools of MS Excel and MATLAB were used. The performance of these methods is analyzed by using the mostly used data: Iris, HBK, Wood multidimensional data. The data generated by MS Excel and statistical data, taken from real life also were used. The investigations show, that the data visualization by a parallel coordinates method has some advantages comparing with Andrews curves visualization method.

Data mining algorithms analyze and mine databases for discovering implicit, previously unknown and potentially useful knowledge. Frequent pattern mining algorithms discover sets of database items that often occur together. Many of the frequent pattern mining algorithms represent the discovered knowledge in the form of a long textual list containing these sets of frequently co-occurring database items. As the amount of discovered knowledge can be large, it may not be easy for most users to examine and understand such a long textual list of knowledge. In my M.Sc. thesis, I represent both the original database and the discovered knowledge in pictorial form. Specifically, I design a new interactive visualization system for viewing the original transaction data (which are then fed into the frequent pattern mining engine) and for revealing the interesting knowledge discovered from the transaction data in the form of mined patterns.

Data mining algorithms analyze and mine databases for discovering implicit, previously unknown and potentially useful knowledge. Frequent pattern mining algorithms discover sets of database items that often occur together. Many of the frequent pattern mining algorithms represent the discovered knowledge in the form of a long textual list containing these sets of frequently co-occurring database items. As the amount of discovered knowledge can be large, it may not be easy for most users to examine and understand such a long textual list of knowledge. In my M.Sc. thesis, I represent both the original database and the discovered knowledge in pictorial form. Specifically, I design a new interactive visualization system for viewing the original transaction data (which are then fed into the frequent pattern mining engine) and for revealing the interesting knowledge discovered from the transaction data in the form of mined patterns.

Honors Project--Smith College, Northampton, Mass., 2009. / Includes bibliographical references.

Thesis (Ph. D.)--Computing, Georgia Institute of Technology, 2006. / Stasko, John, Committee Member ; Owen, Henry, Committee Member ; Merkle, Ralph, Committee Member ; Lee, Wenke, Committee Member ; Ahamad, Mustaque, Committee Chair.

The most important resources to fulfill today’s energy demands are fossil fuels, such as oil and natural gas. When exploiting hydrocarbon reservoirs, a detailed and credible model of the subsurface structures to plan the path of the borehole, is crucial in order to minimize economic and ecological risks. Before that, the placement, as well as the operations of oil rigs need to be planned carefully, as off-shore oil exploration is vulnerable to hazards caused by strong currents. The oil and gas industry therefore relies on accurate ocean forecasting systems for planning their operations. This thesis presents visual workflows for creating subsurface models as well as planning the placement and operations of off-shore structures. Creating a credible subsurface model poses two major challenges: First, the structures in highly ambiguous seismic data are interpreted in the time domain. Second, a velocity model has to be built from this interpretation to match the model to depth measurements from wells. If it is not possible to obtain a match at all positions, the interpretation has to be updated, going back to the first step. This results in a lengthy back and forth between the different steps, or in an unphysical velocity model in many cases. We present a novel, integrated approach to interactively creating subsurface models from reflection seismics, by integrating the interpretation of the seismic data using an interactive horizon extraction technique based on piecewise global optimization with velocity modeling. Computing and visualizing the effects of changes to the interpretation and velocity model on the depth-converted model, on the fly enables an integrated feedback loop that enables a completely new connection of the seismic data in time domain, and well data in depth domain. For planning the operations of off-shore structures we present a novel integrated visualization system that enables interactive visual analysis of ensemble simulations used in ocean forecasting, i.e, simulations of sea surface elevation. Changes in sea surface elevation are a good indicator for the movement of loop current eddies. Our visualization approach enables their interactive exploration and analysis. We enable analysis of the spatial domain, for planning the placement of structures, as well as detailed exploration of the temporal evolution at any chosen position, for the prediction of critical ocean states that require the shutdown of rig operations. We illustrate this using a real-world simulation of the Gulf of Mexico.