Abstract-Mapping a set of categorical values to different colors is an elementary technique in data visualization. Users of visualization software routinely rely on the default colormaps provided by a system, or colormaps suggested by software such as ColorBrewer. In practice, users often have to select a set of colors in a semantically meaningful way (e.g., based on conventions, color metaphors, and logological associations), and consequently would like to ensure their perceptual differentiation is optimized. In this paper, we present an algorithmic approach for maximizing the perceptual distances among a set of given colors. We address two technical problems in optimization, i.e., (i) the phenomena of local maxima that halt the optimization too soon, and (ii) the arbitrary reassignment of colors that leads to the loss of the original semantic association. We paid particular attention to different types of constraints that users may wish to impose during the optimization process. To demonstrate the effectiveness of this work, we tested this technique in two case studies. To reach out to a wider range of users, we also developed a web application called Colourmap Hospital.

Abstract. Visualization is an important task in data analytics, as it allows researchers to view patterns within the data instead of reading through extensive raw data. Allowing the ability to interact with the visualizations is an essential aspect, since it provides the ability to intuitively explore data to find meaning and patterns more efficiently. Interactivity, however, becomes progressively more difficult as the size of the dataset increases. This project begins by leveraging existing web-based data visualization technologies, and extends their functionality through the use of parallel processing. This methodology utilizes state-of-the-art techniques, such as Node.js, to split the visualization rendering and user interactivity controls between a client-server infrastructure without having to rebuild the visualization technologies. The approach minimizes data transfer by performing the rendering step on the server while allowing for the use of high-performance computing systems to render the visualizations more quickly. In order to improve the scaling of the system with larger datasets, parallel processing and visualization optimization techniques are used. This work uses parameter space data generated from mindmodeling.org to showcase the authors' methodology for handling large-scale datasets while retaining interactivity and user friendliness. INTRODUCTIONThe ability to make rapid visual assessments of parameter spaces has the potential to change the workflow for both model simulation and model fitting/parameter recovery. It enables the rapid identification of input parameters that result in similar output data or model behaviors. This allows researchers to eliminate redundant input parameters for more efficient use of modeling and simulation computational resources. For example, should two parameters exhibit a strong correlation, one might be held constant while the other is varied in order to capture all of the unique

When U. S. Government drawings specifications, or other data are used for any purpose other than a definitely related Government procurement operation, the Government thereby incurs no responsibility nor any obligation whatsoever, and the fact that the Government may have formulated, furnished, or in an)" way supplied the said drawings, specifications, or other data, is not to be regarded by implicat'ion or otherwise, or in any manner licensing the holder or any other person or corporation, or conveying any rights or permission to manufacture, use, or sell any patented invention that may in any way be related thereto. Qualified users may obtain copies of this report from the Defense Documentation Center. References to named commercial products in this report are not to be considered in any sense as an I endorsement of the product by the United States Air Force or the Government. [ Defense Docum,mtation Center release to the Clearinghouse for Federal Scientific and Technical Information (CFSTI) and foreign announcement and distribution of this report are not authorized. The distribution of this report is limited because it contains technology identifiable with items excluded from export by the Department of State.

Abstract. Visualization is an important task in data analytics, as it allows researchers to view patterns within the data instead of reading through extensive raw data. Allowing the ability to interact with the visualizations is an essential aspect, since it provides the ability to intuitively explore data to find meaning and patterns more efficiently. Interactivity, however, becomes progressively more difficult as the size of the dataset increases. This project begins by leveraging existing web-based data visualization technologies, and extends their functionality through the use of parallel processing. This methodology utilizes state-of-the-art techniques, such as Node.js, to split the visualization rendering and user interactivity controls between a client-server infrastructure without having to rebuild the visualization technologies. The approach minimizes data transfer by performing the rendering step on the server while allowing for the use of high-performance computing systems to render the visualizations more quickly. In order to improve the scaling of the system with larger datasets, parallel processing and visualization optimization techniques are used. This work uses parameter space data generated from mindmodeling.org to showcase the authors' methodology for handling large-scale datasets while retaining interactivity and user friendliness. INTRODUCTIONThe ability to make rapid visual assessments of parameter spaces has the potential to change the workflow for both model simulation and model fitting/parameter recovery. It enables the rapid identification of input parameters that result in similar output data or model behaviors. This allows researchers to eliminate redundant input parameters for more efficient use of modeling and simulation computational resources. For example, should two parameters exhibit a strong correlation, one might be held constant while the other is varied in order to capture all of the unique

No abstract