DevLan/vispubs huggingface dataset:
An exploratory study of co-located collaborative visual analytics around a tabletop display
Authors: Petra Isenberg, Danyel Fisher, Meredith Ringel Morris, Kori Inkpen, Mary Czerwinski
Published at VAST, 2010

Co-located collaboration can be extremely valuable during complex visual analytics tasks. This paper presents an exploratory study of a system designed to support collaborative visual analysis tasks on a digital tabletop display. Fifteen participant pairs employed Cam-biera, a visual analytics system, to solve a problem involving 240 digital documents. Our analysis, supported by observations, system logs, questionnaires, and interview data, explores how pairs approached the problem around the table. We contribute a unique, rich understanding of how users worked together around the table and identify eight types of collaboration styles that can be used to identify how closely people work together while problem solving. We show how the closeness of teams' collaboration influenced how well they performed on the task overall. We further discuss the role of the tabletop for visual analytics tasks and derive novel design implications for future co-located collaborative tabletop problem solving systems.
Anomaly detection in GPS data based on visual analytics
Authors: Zicheng Liao, Yizhou Yu, Baoquan Chen
Published at VAST, 2010

Modern machine learning techniques provide robust approaches for data-driven modeling and critical information extraction, while human experts hold the advantage of possessing high-level intelligence and domain-specific expertise. We combine the power of the two for anomaly detection in GPS data by integrating them through a visualization and human-computer interaction interface. In this paper we introduce GPSvas (GPS Visual Analytics System), a system that detects anomalies in GPS data using the approach of visual analytics: a conditional random field (CRF) model is used as the machine learning component for anomaly detection in streaming GPS traces. A visualization component and an interactive user interface are built to visualize the data stream, display significant analysis results (i.e., anomalies or uncertain predications) and hidden information extracted by the anomaly detection model, which enable human experts to observe the real-time data behavior and gain insights into the data flow. Human experts further provide guidance to the machine learning model through the interaction tools; the learning model is then incrementally improved through an active learning procedure.
Click2Annotate: Automated Insight Externalization with rich semantics
Authors: Yang Chen, Scott Barlowe, Jing Yang 0001
Published at VAST, 2010

Insight Externalization (IE) refers to the process of capturing and recording the semantics of insights in decision making and problem solving. To reduce human effort, Automated Insight Externalization (AIE) is desired. Most existing IE approaches achieve automation by capturing events (e.g., clicks and key presses) or actions (e.g., panning and zooming). In this paper, we propose a novel AIE approach named Click2Annotate. It allows semi-automatic insight annotation that captures low-level analytics task results (e.g., clusters and outliers), which have higher semantic richness and abstraction levels than actions and events. Click2Annotate has two significant benefits. First, it reduces human effort required in IE and generates annotations easy to understand. Second, the rich semantic information encoded in the annotations enables various insight management activities, such as insight browsing and insight retrieval. We present a formal user study that proved this first benefit. We also illustrate the second benefit by presenting the novel insight management activities we developed based on Click2Annotate, namely scented insight browsing and faceted insight search.
Cluster correspondence views for enhanced analysis of SOM displays
Authors: Jürgen Bernard, Tatiana von Landesberger, Sebastian Bremm, Tobias Schreck
Published at VAST, 2010

The Self-Organizing Map (SOM) algorithm is a popular and widely used cluster algorithm. Its constraint to organize clusters on a grid structure makes it very amenable to visualization. On the other hand, the grid constraint may lead to reduced cluster accuracy and reliability, compared to other clustering methods not implementing this restriction. We propose a visual cluster analysis system that allows to validate the output of the SOM algorithm by comparison with alternative clustering methods. Specifically, visual mappings overlaying alternative clustering results onto the SOM are proposed. We apply our system on an example data set, and outline main analytical use cases.
Combining statistical independence testing, visual attribute selection and automated analysis to find relevant attributes for classification
Authors: Thorsten May, James Davey, Jörn Kohlhammer
Published at VAST, 2010

We present an iterative strategy for finding a relevant subset of attributes for the purpose of classification in high-dimensional, heterogeneous data sets. The attribute subset is used for the construction of a classifier function. In order to cope with the challenge of scalability, the analysis is split into an overview of all attributes and a detailed analysis of small groups of attributes. The overview provides generic information on statistical dependencies between attributes. With this information the user can select groups of attributes and an analytical method for their detailed analysis. The detailed analysis involves the identification of redundant attributes (via classification or regression) and the creation of summarizing attributes (via clustering or dimension reduction). Our strategy does not prescribe specific analytical methods. Instead, we recursively combine the results of different methods to find or generate a subset of attributes to use for classification.
Comparing different levels of interaction constraints for deriving visual problem isomorphs
Authors: Wenwen Dou, Caroline Ziemkiewicz, Lane Harrison, Dong Hyun Jeong, Roxanne Ryan, William Ribarsky, Derek Xiaoyu Wang, Remco Chang
Published at VAST, 2010

Interaction and manual manipulation have been shown in the cognitive science literature to play a critical role in problem solving. Given different types of interactions or constraints on interactions, a problem can appear to have different degrees of difficulty. While this relationship between interaction and problem solving has been well studied in the cognitive science literatures, the visual analytics community has yet to exploit this understanding for analytical problem solving. In this paper, we hypothesize that constraints on interactions and constraints encoded in visual representations can lead to strategies of varying effectiveness during problem solving. To test our hypothesis, we conducted a user study in which participants were given different levels of interaction constraints when solving a simple math game called Number Scrabble. Number Scrabble is known to have an optimal visual problem isomorph, and the goal of this study is to learn if and how the participants could derive the isomorph and to analyze the strategies that the participants utilize in solving the problem. Our results indicate that constraints on interactions do affect problem solving, and that while the optimal visual isomorph is difficult to derive, certain interaction constraints can lead to a higher chance of deriving the isomorph.
Conveying network features in geospatial battlespace displays
Authors: J. Alex Godwin, Ryan M. Kilgore
Published at VAST, 2010

Advanced battlespace network visualization techniques are required within the modern Air Operations Center (AOC) to improve cross-domain situation awareness and to support planning and decision-making. We present a visualization toolkit to address this need that supports the integration of network health and status information and meta-information with other traditional AOC information resources and activities across air, space, and cyber domains. Applications include the development of battlespace visualization technologies that will improve warfighters' decision-making response time and provide enhanced flexibility for mission planning by efficiently revealing affordances for leveraging, disrupting, or enhancing network connectivity.
Data representation and exploration with Geometric Wavelets
Authors: Eric E. Monson, Guangliang Chen, Rachel Brady, Mauro Maggioni
Published at VAST, 2010

Geometric Wavelets is a new multi-scale data representation technique which is useful for a variety of applications such as data compression, interpretation and anomaly detection. We have developed an interactive visualization with multiple linked views to help users quickly explore data sets and understand this novel construction. Currently the interface is being used by applied mathematicians to view results and gain new insights, speeding methods development.
Diamonds in the rough: Social media visual analytics for journalistic inquiry
Authors: Nicholas Diakopoulos, Mor Naaman, Funda Kivran-Swaine
Published at VAST, 2010

Journalists increasingly turn to social media sources such as Facebook or Twitter to support their coverage of various news events. For large-scale events such as televised debates and speeches, the amount of content on social media can easily become overwhelming, yet still contain information that may aid and augment reporting via individual content items as well as via aggregate information from the crowd's response. In this work we present a visual analytic tool, Vox Civitas, designed to help journalists and media professionals extract news value from large-scale aggregations of social media content around broadcast events. We discuss the design of the tool, present the text analysis techniques used to enable the presentation, and provide details on the visual and interaction design. We provide an exploratory evaluation based on a user study in which journalists interacted with the system to explore and report on a dataset of over one hundred thousand twitter messages collected during the U.S. State of the Union presidential address in 2010.
DimStiller: Workflows for dimensional analysis and reduction
Authors: Stephen Ingram, Tamara Munzner, Veronika Irvine, Melanie Tory, Steven Bergner, Torsten Möller
Published at VAST, 2010

DimStiller is a system for dimensionality reduction and analysis. It frames the task of understanding and transforming input dimensions as a series of analysis steps where users transform data tables by chaining together different techniques, called operators, into pipelines of expressions. The individual operators have controls and views that are linked together based on the structure of the expression. Users interact with the operator controls to tune parameter choices, with immediate visual feedback guiding the exploration of local neighborhoods of the space of possible data tables. DimStiller also provides global guidance for navigating data-table space through expression templates called workflows, which permit re-use of common patterns of analysis.
Discovering bits of place histories from people's activity traces
Authors: Gennady L. Andrienko, Natalia V. Andrienko, Martin Mladenov, Michael Mock, Christian Pölitz
Published at VAST, 2010

Events that happened in the past are important for understanding the ongoing processes, predicting future developments, and making informed decisions. Significant and/or interesting events tend to attract many people. Some people leave traces of their attendance in the form of computer-processable data, such as records in the databases of mobile phone operators or photos on photo sharing web sites. We developed a suite of visual analytics methods for reconstructing past events from these activity traces. Our tools combine geocomputations, interactive geovisualizations and statistical methods to enable integrated analysis of the spatial, temporal, and thematic components of the data, including numeric attributes and texts. We demonstrate the utility of our approach on two large real data sets, mobile phone calls in Milano during 9 days and flickr photos made on British Isles during 5 years.
EmailTime: Visual analytics of emails
Authors: Minoo Erfani Joorabchi, Ji-Dong Yim, Chris Shaw 0002
Published at VAST, 2010

Although the discovery and analysis of communication patterns in large and complex email datasets are difficult tasks, they can be a valuable source of information. This paper presents EmailTime's capabilities through several examples. EmailTime is a visual analysis of email correspondence patterns over the course of time that interactively portrays personal and interpersonal networks using the correspondence in the email dataset. We suggest that integrating both statistics and visualizations in order to display information about the email datasets may simplify its evaluation.
Enhancing text-based chat with visuals for hazardous weather decision making
Authors: Moshe Gutman, Gina Eosco, Monica Zappa, Chris E. Weaver
Published at VAST, 2010

We created a visual chat application for use during hazardous weather events. The application, NWSChat2, allows National Weather Service forecasters, media members, and storm trackers to communicate with each other, basing their conversation on a common shared radar map of the storm. Users can additionally annotate the map with `pins' or draw notes with a stylus. These annotations are automatically shared with all other users. The collaborative nature of NWSChat2 makes it well-suited for disseminating information to all users during weather emergencies.
Enron case study: Analysis of email behavior using EmailTime
Authors: Minoo Erfani Joorabchi, Ji-Dong Yim, Mona Erfani Joorabchi, Chris Shaw 0002
Published at VAST, 2010

This paper presents a case study with Enron email dataset to explore the behaviors of email users within different organizational positions. We defined email behavior as the email activity level of people regarding a series of measured metrics e.g. sent and received emails, numbers of email addresses, etc. These metrics were calculated through EmailTime, a visual analysis tool of email correspondence over the course of time. Results showed specific patterns in the email datasets of different organizational positions.
finding and visualizing relevant subspaces for clustering high-dimensional astronomical data using connected morphological operators
Authors: Bilkis J. Ferdosi, Hugo Buddelmeijer, Scott C. Trager, Michael H. F. Wilkinson, Jos B. T. M. Roerdink
Published at VAST, 2010

Data sets in astronomy are growing to enormous sizes. Modern astronomical surveys provide not only image data but also catalogues of millions of objects (stars, galaxies), each object with hundreds of associated parameters. Exploration of this very high-dimensional data space poses a huge challenge. Subspace clustering is one among several approaches which have been proposed for this purpose in recent years. However, many clustering algorithms require the user to set a large number of parameters without any guidelines. Some methods also do not provide a concise summary of the datasets, or, if they do, they lack additional important information such as the number of clusters present or the significance of the clusters. In this paper, we propose a method for ranking subspaces for clustering which overcomes many of the above limitations. First we carry out a transformation from parametric space to discrete image space where the data are represented by a grid-based density field. Then we apply so-called connected morphological operators on this density field of astronomical objects that provides visual support for the analysis of the important subspaces. Clusters in subspaces correspond to high-intensity regions in the density image. The importance of a cluster is measured by a new quality criterion based on the dynamics of local maxima of the density. Connected operators are able to extract such regions with an indication of the number of clusters present. The subspaces are visualized during computation of the quality measure, so that the user can interact with the system to improve the results. In the result stage, we use three visualization toolkits linked within a graphical user interface so that the user can perform an in-depth exploration of the ranked subspaces. Evaluation based on synthetic as well as real astronomical datasets demonstrates the power of the new method. We recover various known astronomical relations directly from the data with little or no a priori assumptions. Hence, our method holds good prospects for discovering new relations as well.
Flow-based scatterplots for sensitivity analysis
Authors: Yu-Hsuan Chan, Carlos D. Correa, Kwan-Liu Ma
Published at VAST, 2010

Visualization of multi-dimensional data is challenging due to the number of complex correlations that may be present in the data but that are difficult to be visually identified. One of the main causes for this problem is the inherent loss of information that occurs when high-dimensional data is projected into 2D or 3D. Although 2D scatterplots are ubiquitous due to their simplicity and familiarity, there are not a lot of variations on their basic metaphor. In this paper, we present a new way of visualizing multidimensional data using scatterplots. We extend 2D scatterplots using sensitivity coefficients to highlight local variation of one variable with respect to another. When applied to a scatterplot, these sensitivities can be understood as velocities, and the resulting visualization resembles a flow field. We also present a number of operations, based on flow-field analysis, that help users navigate, select and cluster points in an efficient manner. We show the flexibility and generality of this approach using a number of multidimensional data sets across different domains.
Geo-historical context support for information foraging and sensemaking: Conceptual model, implementation, and assessment
Authors: Brian M. Tomaszewski, Alan M. MacEachren
Published at VAST, 2010

Information foraging and sensemaking with heterogeneous information are context-dependent activities. Thus visual analytics tools to support these activities must incorporate context. But, context is a difficult concept to define, model, and represent. Creating and representing context in support of visually-enabled reasoning about complex problems with complex information is a complementary but different challenge than that addressed in context-aware computing. In the latter, the goal is automated adaptation of the system to meet user needs for applications such as mobile location-based services where information about the location, the user, and the user goals filters what gets presented on a small mobile device. In contrast, for visual analytics-enabled information foraging and sensemaking, the user is likely to take an active role in foraging for the contextual information needed to support sensemaking in relation to some multifaceted problem. In this paper, we address the challenges of constructing and representing context within visual interfaces that support analytical reasoning in crisis management and humanitarian relief. The challenges stem from the diverse forms of information that can provide context and difficulty in defining and operationalizing context itself. Here, we pay particular attention to document foraging to support construction of the geographic and historical context within which monitoring and sensemaking can be carried out. Specifically, we present the concept of geo-historical context (GHC) and outline an empirical assessment of both the concept and its implementation in the Context Discovery Application, a web-based tool that supports document foraging and sensemaking.
Helping users recall their reasoning process
Authors: Heather Lipford, Felesia Stukes, Wenwen Dou, Matthew E. Hawkins, Remco Chang
Published at VAST, 2010

The final product of an analyst's investigation using a visualization is often a report of the discovered knowledge, as well as the methods employed and reasoning behind the discovery. We believe that analysts may have difficulty keeping track of their knowledge discovery process and will require tools to assist in accurately recovering their reasoning. We first report on a study examining analysts' recall of their strategies and methods, demonstrating their lack of memory of the path of knowledge discovery. We then explore whether a tool visualizing the steps of the visual analysis can aid users in recalling their reasoning process. The results of our second study indicate that visualizations of interaction logs can serve as an effective memory aid, allowing analysts to recall additional details of their strategies and decisions.
Improving the visual analysis of high-dimensional datasets using quality measures
Authors: Georgia Albuquerque, Martin Eisemann, Dirk J. Lehmann, Holger Theisel, Marcus A. Magnor
Published at VAST, 2010

Modern visualization methods are needed to cope with very high-dimensional data. Efficient visual analytical techniques are required to extract the information content in these data. The large number of possible projections for each method, which usually grow quadrat-ically or even exponentially with the number of dimensions, urges the necessity to employ automatic reduction techniques, automatic sorting or selecting the projections, based on their information-bearing content. Different quality measures have been successfully applied for several specified user tasks and established visualization techniques, like Scatterplots, Scatterplot Matrices or Parallel Coordinates. Many other popular visualization techniques exist, but due to the structural differences, the measures are not directly applicable to them and new approaches are needed. In this paper we propose new quality measures for three popular visualization methods: Radviz, Pixel-Oriented Displays and Table Lenses. Our experiments show that these measures efficiently guide the visual analysis task.
Interactive querying of temporal data using a comic strip metaphor
Authors: Jing Jin, Pedro A. Szekely
Published at VAST, 2010

Finding patterns in temporal data is an important data analysis task in many domains. Static visualizations can help users easily see certain instances of patterns, but are not specially designed to support systematic analysis tasks, such as finding all instances of a pattern automatically. VizPattern is an interactive visual query environment that uses a comic strip metaphor to enable users to easily and quickly define and locate complex temporal patterns. Evaluations provide evidence that VizPattern is applicable in many domains, and that it enables a wide variety of users to answer questions about temporal data faster and with fewer errors than existing state-of-the-art visual analysis systems.
Interactive visual analysis of multiobjective optimizations
Authors: Wolfgang Berger, Harald Piringer
Published at VAST, 2010

Optimization problems are typically addressed by purely automatic approaches. For multi-objective problems, however, a single best solution often does not exist. In this case, it is necessary to analyze trade-offs between many conflicting goals within a given application context. This poster describes an approach that tightly integrates automatic algorithms for multi-objective optimization and interactive multivariate visualizations. Ad-hoc selections support a flexible definition of input data for subsequent algorithms. These algorithms in turn represent their result as derived data attributes that can be assigned to visualizations or be used as a basis for further selections (e.g., to constrain the result set). This enables a guided search that still involves the knowledge of domain experts. We describe our approach in the context of multi-run simulation data from the application domain of car engine design.