Slycat Analysis and Visualization
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We present the results of the first stage of a two-stage evaluation of open source visual analytics packages. This stage is a broad feature comparison over a range of open source toolkits. Although we had originally intended to restrict ourselves to comparing visual analytics toolkits, we quickly found that very few were available. So we expanded our study to include information visualization, graph analysis, and statistical packages. We examine three aspects of each toolkit: visualization functions, analysis capabilities, and development environments. With respect to development environments, we look at platforms, language bindings, multi-threading/parallelism, user interface frameworks, ease of installation, documentation, and whether the package is still being actively developed.
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This report is a summary of the accomplishments of the 'Scalable Solutions for Processing and Searching Very Large Document Collections' LDRD, which ran from FY08 through FY10. Our goal was to investigate scalable text analysis; specifically, methods for information retrieval and visualization that could scale to extremely large document collections. Towards that end, we designed, implemented, and demonstrated a scalable framework for text analysis - ParaText - as a major project deliverable. Further, we demonstrated the benefits of using visual analysis in text analysis algorithm development, improved performance of heterogeneous ensemble models in data classification problems, and the advantages of information theoretic methods in user analysis and interpretation in cross language information retrieval. The project involved 5 members of the technical staff and 3 summer interns (including one who worked two summers). It resulted in a total of 14 publications, 3 new software libraries (2 open source and 1 internal to Sandia), several new end-user software applications, and over 20 presentations. Several follow-on projects have already begun or will start in FY11, with additional projects currently in proposal.
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Proposed for publication in Coordinated & Multiple Views in Exploratory Visualization, Special Issue of Information Visualization Journal, Vol 2 No. 4, Palgrave/Macmillan.
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This case study provides examples of how some simple decisions the authors made in structuring their algorithms for handling cell-centered data can dramatically influence the results. Although they all know that these decisions produce variations in results, they think that they underestimate the potential magnitude of the differences. More importantly, the users of the codes may not be aware that these choices have been made or what they mean to the resulting visualizations of their data. This raises the question of whether or not these decisions are inadvertently distorting user interpretations of data sets.