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Authors
Steffen Koch
Harald Bosch
Mark Giereth
Thomas Ertl

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Abstract
Patents are an important economic factor in today’s globalized markets. Therefore, the analysis of patent information has become an inevitable task for a variety of interest groups. The retrieval of relevant patent information is an integral part of almost every patent analysis scenario. Unfortunately, the complexity of patent material inhibits a straightforward retrieval of all relevant patent documents and leads to iterative, time-consuming approaches in practice. With ‘PatViz’, a new system for interactive analysis of patent information has been developed to leverage iterative query refinement. PatViz supports users in building complex queries visually and in exploring patent result sets interactively. Thereby, the visual query module introduces an abstraction layer that provides uniform access to different retrieval systems and relieves users of the burden to learn different complex query languages. By establishing an integrated environment it allows for interactive reintegration of insights gained from visual result set exploration into the visual query representation. We expect that the approach we have taken is also suitable to improve iterative query refinement in other Visual Analytics systems.

Index Terms
H.5.2 [Information Interfaces and Presentation (e.g.HCI)]: User Interfaces—Graphical user interfaces (GUI) H.3.3 [Information Storage and Retrieval]: Information Search and Retrieval—Search process

Authors
Stephen Rudolph
Anya Savikhin
David S. Ebert

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Abstract
FinVis is a visual analytics tool that allows the non-expert casual user to interpret the return, risk and correlation aspects of financial data and make personal finance decisions. This interactive exploratory tool helps the casual decision-maker quickly choose between various financial portfolio options and view possible outcomes. FinVis allows for exploration of inter-temporal data to analyze outcomes of short-term or long-term investment decisions. FinVis helps the user overcome cognitive limitations and understand the impact of correlation between financial instruments in order to reap the benefits of portfolio diversification. Because this software is accessible by non-expert users, decision-makers from the general population can benefit greatly from using FinVis in practical applications. We quantify the value of FinVis using experimental economics methods and find that subjects using the FinVis software make better financial portfolio decisions as compared to subjects using a tabular version with the same information. We also find that FinVis engages the user, which results in greater exploration of the dataset and increased learning as compared to a tabular display. Further, participants using FinVis reported increased confidence in financial decision-making and noted that they were likely to use this tool in practical application.

Index Terms
J.1 [Administrative Data Processing]: Financial (e.g., EFTS)—; I.6.8 [Types of Simulation]: Visual—

Authors
Daniela Oelke
Ming Hao
Christian Rohrdantz
Daniel A. Keim
Umeshwar Dayal
Lars-Erik Haug
Halldór Janetzko

Abstract
Today, online stores collect a lot of customer feedback in the form of surveys, reviews, and comments. This feedback is categorized and in some cases responded to, but in general it is underutilized – even though customer satisfaction is essential to the success of their business. In this paper, we introduce several new techniques to interactively analyze customer comments and ratings to determine the positive and negative opinions expressed by the customers. First, we introduce a new discrimination-based technique to automatically extract the terms that are the subject of the positive or negative opinion (such as price or customer service) and that are frequently commented on. Second, we derive a Reverse-Distance-Weighting method to map the attributes to the related positive and negative opinions in the text. Third, the resulting high-dimensional feature vectors are visualized in a new summary representation that provides a quick overview. We also cluster the reviews according to the similarity of the comments. Special thumbnails are used to provide insight into the composition of the clusters and their relationship. In addition, an interactive circular correlation map is provided to allow analysts to detect the relationships of the comments to other important attributes and the scores. We have applied these techniques to customer comments from real-world online stores and product reviews from web sites to identify the strength and problems of different products and services, and show the potential of our technique.

Index Terms
I.7.5 [Document and Text Processing]: Document Capture - Document Analysis; I.5.2 [Pattern Recognition]: Design Methodology - Feature evaluation and selection

Authors
Janko Dietzsch
Julian Heinrich
Kay Nieselt
Dirk Bartz

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Abstract
We present a new application, SpRay, designed for the visual exploration of gene expression data. It is based on an extension and adaption of parallel coordinates to support the visual exploration of large and high-dimensional datasets. In particular, we investigate the visual analysis of gene expression data as generated by microarray experiments; We combine refined visual exploration with statistical methods to a visual analytics approach that proved to be particularly successful in this application domain. We will demonstrate the usefulness on several multidimensional gene expression datasets from different bioinformatics applications.

Index Terms
I.3.3 [Computer Graphics]: Line and Curve Generation, Display Algorithms I.3.6 [Computer Graphics]: Interaction Techniques J.3 [Life and Medical Sciences]: Biology and Genetics

Authors
Boonthanome Nouanesengsy
Sang-Cheol Seok
Han-Wei Shen
Veronica J Vieland

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Running Time: 17 min 41 sec

Abstract
Gene mapping is a statistical method used to localize human disease genes to particular regions of the human genome. When performing such analysis, a genetic likelihood space is generated and sampled, which results in a multidimensional scalar field. Researchers are interested in exploring this likelihood space through the use of visualization. Previous efforts at visualizing this space, though, were slow and cumbersome, only showing a small portion of the space at a time, thus requiring the user to keep a mental picture of several views. We have developed a new technique that displays much more data at once by projecting the multidimensional data into several 2D plots. One plot is created for each parameter that shows the change along that parameter. A radial projection is used to create another plot that provides an overview of the high dimensional surface from the perspective of a single point. Linking and brushing between all the plots are used to determine relationships between parameters. We demonstrate our techniques on real world autism data, showing how to visually examine features of the high dimensional space.

Authors
Robert Kincaid
Kurt Dejgaard

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Running Time: 21 min 40 sec

Abstract
Protein complexes are formed when two or more proteins noncovalently interact to form a larger three dimensional structure with specific biological function. Understanding the composition of such complexes is vital to understanding cell biology at the molecular level. MassVis is a visual analysis tool designed to assist the interpretation of data from a new workflow for detecting the composition of such protein complexes in biological samples. The data generated by the laboratory workflow naturally lends itself to a scatter plot visualization. However, characteristics of this data give rise to some unique aspects not typical of a standard scatter plot. We are able to take the output from tandem mass spectrometry and render the data in such a way that it mimics more traditional two-dimensional gel techniques and at the same time reveals the correlated behavior indicative of protein complexes. By computationally measuring these correlated patterns in the data, membership in putative complexes can be inferred. User interactions are provided to support both an interactive discovery mode as well as an unsupervised clustering of likely complexes. The specific analysis tasks led us to design a unique arrangement of item selection and coordinated detail views in order to simultaneously view different aspects of the selected item.

Index Terms
H.1.2 [User/Machine Systems]: Human information processing – Visual Analytics; I.6.9 [Visualization]: information visualization; J.3 [Life and Medical Sciences].

Authors
Tatiana von Landesberger
Melanie Görner
Tobias Schreck

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Abstract
In this paper, we present a system for the interactive visualization and exploration of graphs with many weakly connected components. The visualization of large graphs has recently received much research attention. However, specific systems for visual analysis of graph data sets consisting of many components are rare. In our approach, we rely on graph clustering using an extensive set of topology descriptors. Specifically, we use the Self-Organizing-Map algorithm in conjunction with a user-adaptable combination of graph features for clustering of graphs. It offers insight into the overall structure of the data set. The clustering output is presented in a grid containing clusters of the connected components of the input graph. Interactive feature selection and task-tailored data views allow the exploration of the whole graph space. The system provides also tools for assessment and display of cluster quality. We demonstrate the usefulness of our system by application to a shareholder network analysis problem based on a large real-world data set. While so far our approach is applied to weighted directed graphs only, it can be used for various graph types.

Index Terms
E.1 [Data Structures]: Graphs and Networks—[H.3.3]: Information Search and Retrieval—Clustering H.5.2 [User Interfaces]: Graphical user interfaces (GUI)— [I.3.3]: COMPUTER GRAPHICS—Picture/Image Generation

Authors
Pak Chung Wong
Patrick Mackey
Kristin A. Cook
Randall M. Rohrer
Harlan Foote
Mark A. Whiting

Abstract
This paper presents a working graph analytics model that embraces the strengths of the traditional top-down and bottom-up approaches with a resilient crossover concept to exploit the vast middle-ground information overlooked by the two extreme analytical approaches. Our graph analytics model is co-developed by users and researchers, who carefully studied the functional requirements that reflect the critical thinking and interaction pattern of a real-life intelligence analyst. To evaluate the model, we implement a system prototype, known as GreenHornet, which allows our analysts to test the theory in practice, identify the technological and usage-related gaps in the model, and then adapt the new technology in their work space. The paper describes the implementation of GreenHornet and compares its strengths and weaknesses against the other prevailing models and tools.

Index Terms
H.1.2 [User/Machine Systems]: Human Information Processing – Visual Analytics; I.6.9 [Visualization]: information visualization.

Authors
Youn-ah Kang
Carsten Görg
John Stasko

Abstract
Despite the growing number of systems providing visual analytic support for investigative analysis, few empirical studies of the potential benefits of such systems have been conducted, particularly controlled, comparative evaluations. Determining how such systems foster insight and sensemaking is important for their continued growth and study, however. Furthermore, studies that identify how people use such systems and why they benefit (or not) can help inform the design of new systems in this area. We conducted an evaluation of the visual analytics system Jigsaw employed in a small investigative sensemaking exercise, and we compared its use to three other more traditional methods of analysis. Sixteen participants performed a simulated intelligence analysis task under one of the four conditions. Experimental results suggest that Jigsaw assisted participants to analyze the data and identify an embedded threat. We describe different analysis strategies used by study participants and how computational support (or the lack thereof) influenced the strategies. We then illustrate several characteristics of the sensemaking process identified in the study and provide design implications for investigative analysis tools based thereon. We conclude with recommendations for metrics and techniques for evaluating other visual analytics investigative analysis tools.