Authors
Nazanin Kadivar
Victor Chen
Dustin Dunsmuir
Eric Lee
Cheryl Qian
John Dill
Christopher Shaw
Robert Woodbury

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Audio Running Time: 17 min 25 sec

Abstract
Visual analytics tools provide powerful visual representations in order to support the sense-making process. In this process, analysts typically iterate through sequences of steps many times, varying parameters each time. Few visual analytics tools support this process well, nor do they provide support for visualizing and understanding the analysis process itself. To help analysts understand, explore, reference, and reuse their analysis process, we present a visual analytics system named CzSaw (See-Saw) that provides an editable and re-playable history navigation channel in addition to multiple visual representations of document collections and the entities within them (in a manner inspired by Jigsaw [24]). Conventional history navigation tools range from basic undo and redo to branching timelines of user actions. In CzSaw’s approach to this, first, user interactions are translated into a script language that drives the underlying scripting-driven propagation system. The latter allows analysts to edit analysis steps, and ultimately to program them. Second, on this base, we build both a history view showing progress and alternative paths, and a dependency graph showing the underlying logic of the analysis and dependency relations among the results of each step. These tools result in a visual model of the sense-making process, providing a way for analysts to visualize their analysis process, to reinterpret the problem, explore alternative paths, extract analysis patterns from existing history, and reuse them with other related analyses.

Index Terms
I.3.8 [Computer Graphics]: Applications-Visual Analytics, I.6.9 [Visualization]: information visualization, H.5.2 [Information Interfaces & Presentations]: User Interfaces - Graphical User Interfaces (GUI)

Authors
Yedendra B. Shrinivasan
David Gotzy
Jie Lu

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Running Time: 19 min 09 sec

Abstract
During visual analysis, users must often connect insights discovered at various points of time. This process is often called “connecting the dots.” When analysts interactively explore complex datasets over multiple sessions, they may uncover a large number of findings. As a result, it is often difficult for them to recall the past insights, views and concepts that are most relevant to their current line of inquiry. This challenge is even more difficult during collaborative analysis tasks where they need to find connections between their own discoveries and insights found by others. In this paper, we describe a context-based retrieval algorithm to identify notes, views and concepts from users’ past analyses that are most relevant to a view or a note based on their line of inquiry. We then describe a related notes recommendation feature that surfaces the most relevant items to the user as they work based on this algorithm. We have implemented this recommendation feature in HARVEST, a web based visual analytic system. We evaluate the related notes recommendation feature of HARVEST through a case study and discuss the implications of our approach.

Index Terms
H.3.3 [Information Search and Retrieval]—Retrieval models

Authors
Mark A. Whiting
Chris North
Alex Endert
Jean Scholtz
Jereme Haack
Carrie Varley
Jim Thomas

Abstract
The IEEE Visual Analytics Science and Technology (VAST) Symposium has held a contest each year since its inception in 2006. These events are designed to provide visual analytics researchers and developers with analytic challenges similar to those encountered by professional information analysts. The VAST contest has had an extended life outside of the symposium, however, as materials are being used in universities and other educational settings, either to help teachers of visual analytics related classes or for student projects. We describe how we develop VAST contest datasets that results in products that can be used in different settings and review some specific examples of the adoption of the VAST contest materials in the classroom. The examples are drawn from graduate and undergraduate courses at Virginia Tech and from the Visual Analytics “Summer Camp” run by the National Visualization and Analytics Center in 2008. We finish with a brief discussion on evaluation metrics for education.

Index Terms
K.3.2 [Computer and Information Science Education]: Curriculum; H.5.1 [Multimedia Information Systems]: Evaluation/Methodology

Authors
Romain Vuillemot
Tanya Clement
Catherine Plaisant
Amit Kumar

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Authors
Stuart Rose
Scott Butner
Wendy Cowley
Michelle Gregory
Julia Walker

Authors
Christopher Collins
Fernanda B. Viégas
Martin Wattenberg

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Running Time: 21 min 02 sec

Abstract
Do court cases differ from place to place? What kind of picture do we get by looking at a country’s collection of law cases? We introduce Parallel Tag Clouds: a new way to visualize differences amongst facets of very large metadata-rich text corpora. We have pointed Parallel Tag Clouds at a collection of over 600,000 US Circuit Court decisions spanning a period of 50 years and have discovered regional as well as linguistic differences between courts. The visualization technique combines graphical elements from parallel coordinates and traditional tag clouds to provide rich overviews of a document collection while acting as an entry point for exploration of individual texts. We augment basic parallel tag clouds with a details-in-context display and an option to visualize changes over a second facet of the data, such as time. We also address text mining challenges such as selecting the best words to visualize, and how to do so in reasonable time periods to maintain interactivity.

Authors
Patricia J. Crossno
Daniel M. Dunlavy
Timothy M. Shead

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Running Time: 20 min 16 sec

Abstract
Latent Semantic Analysis (LSA) is a commonly-used method for automated processing, modeling, and analysis of unstructured text data. One of the biggest challenges in using LSA is determining the appropriate model parameters to use for different data domains and types of analyses. Although automated methods have been developed to make rank and scaling parameter choices, these approaches often make choices with respect to noise in the data, without an understanding of how those choices impact analysis and problem solving. Further, no tools currently exist to explore the relationships between an LSA model and analysis methods. Our work focuses on how parameter choices impact analysis and problem solving. In this paper, we present LSAView, a system for interactively exploring parameter choices for LSA models. We illustrate the use of LSAView’s small multiple views, linked matrix-graph views, and data views to analyze parameter selection and application in the context of graph layout and clustering.

Index Terms
I.3.8 [Computing Methodologies]: Computer Graphics—Applications; I.2.7 [Computing Methodologies]: Natural Language Processing—Text analysis

Abstract
Zhenyu Guo
Matthew O. Ward
Elke A. Rundensteiner

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Authors
Discovering and extracting linear trends and correlations in datasets is very important for analysts to understand multivariate phenomena. However, current widely used multivariate visualization techniques, such as parallel coordinates and scatterplot matrices, fail to reveal and illustrate such linear relationships intuitively, especially when more than 3 variables are involved or multiple trends coexist in the dataset. We present a novel multivariate model parameter space visualization system that helps analysts discover single and multiple linear patterns and extract subsets of data that fit a model well. Using this system, analysts are able to explore and navigate in model parameter space, interactively select and tune patterns, and refine the model for accuracy using computational techniques. We build connections between model space and data space visually, allowing analysts to employ their domain knowledge during exploration to better interpret the patterns they discover and their validity. Case studies with real datasets are used to investigate the effectiveness of the visualizations.

Index Terms
H.5.2 [Information Interfaces and Presentation]: User Interfaces—Graphical user interfaces

Authors
Jaegul Choo
Shawn Bohn
Haesun Park

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Running Time: 18 min 39 sec

Abstract
In this paper, we discuss dimension reduction methods for 2D visualization of high dimensional clustered data. We propose a two stage framework for visualizing such data based on dimension reduction methods. In the first stage, we obtain the reduced dimensional data by applying a supervised dimension reduction method such as linear discriminant analysis which preserves the original cluster structure in terms of its criteria. The resulting optimal reduced dimension depends on the optimization criteria and is often larger than 2. In the second stage, the dimension is further reduced to 2 for visualization purposes by another dimension reduction method such as principal component analysis. The role of the second-stage is to minimize the loss of information due to reducing the dimension all the way to 2. Using this framework, we propose several two-stage methods, and present their theoretical characteristics as well as experimental comparisons on both artificial and real-world text data sets.

Index Terms
H.5.2 [INFORMATION INTERFACES AND PRESENTATION]:User Interfaces—Theory and methods

Authors
Andrada Tatu
Georgia Albuquerque
Martin Eisemann
Jörn Schneidewind
Holger Theisel
Marcus Magnork
Daniel Keim

Abstract
Visual exploration of multivariate data typically requires projection onto lower-dimensional representations. The number of possible representations grows rapidly with the number of dimensions, and manual exploration quickly becomes ineffective or even unfeasible. This paper proposes automatic analysis methods to extract potentially relevant visual structures from a set of candidate visualizations. Based on features, the visualizations are ranked in accordance with a specified user task. The user is provided with a manageable number of potentially useful candidate visualizations, which can be used as a starting point for interactive data analysis. This can effectively ease the task of finding truly useful visualizations and potentially speed up the data exploration task. In this paper, we present ranking measures for class-based as well as non class-based Scatterplots and Parallel Coordinates visualizations. The proposed analysis methods are evaluated on different datasets.

Index Terms: H.3.3 [Information Storage and Retrieval]: Information Search and Retrieval I.3.3 [Computer Graphics]: Picture/Image Generation;