DevLan/vispubs huggingface dataset:
Interactive poster: Visual data mining of unevenly-spaced event sequences
Authors: Alex Godwin, Remco Chang, Robert Kosara, William Ribarsky
Published at VAST, 2008

We present a process for the exploration and analysis of large databases of events. A typical database is characterized by the sequential actions of a number of individual entities. These entities can be compared by their similarities in sequence and changes in sequence over time. The correlation of two sequences can provide important clues as to the possibility of a connection between the responsible entities, but an analyst might not be able to specify the type of connection sought prior to examination. Our process incorporates extensive automated calculation and data mining but permits diversity of analysis by providing visualization of results at multiple levels, taking advantage of human intuition and visual processing to generate avenues of inquiry.
Maintaining interactivity while exploring massive time series
Authors: Sye-Min Chan, Ling Xiao, John Gerth, Pat Hanrahan
Published at VAST, 2008

The speed of data retrieval qualitatively affects how analysts visually explore and analyze their data. To ensure smooth interactions in massive time series datasets, one needs to address the challenges of computing &lt;i&gt;ad&lt;/i&gt; &lt;i&gt;hoc&lt;/i&gt; queries, distributing query load, and hiding system latency. In this paper, we present ATLAS, a visualization tool for temporal data that addresses these issues using a combination of high performance database technology, predictive caching, and level of detail management. We demonstrate ATLAS using commodity hardware on a network traffic dataset of more than a billion records.
Migrant boat mini challenge award: Analysis summary a geo-temporal analysis of the migrant boat dataset
Authors: Benjamin Holland, Lisa Kuchy, Jason Dalton
Published at VAST, 2008

The SPADAC team used various visual analytics tools and methods to find geo-temporal patterns of migration from a Caribbean island from 2005-2007. In this paper, we describe the tools and methods used in the analysis. These methods included generating temporal variograms, dendrograms, and proportionally weighted migration maps, using tools such as the R statistical software package and Signature Analysttrade. We found that there is a significant positive space-time correlation with the boat encounters (especially the landings), with a migratory shift further away from the point of departure over time.
Migrant boat mini challenge award: Simple and effective integrated display geo-temporal analysis of migrant boats
Authors: Ranko Miklin, Tomislav Lipic, Zoltan Konyha, Mario Beric, Wolfgang Freiler, Kresimir Matkovic, Denis Gracanin
Published at VAST, 2008

We provide a description of the tools and techniques used in our analysis of the VAST 2008 Challenge dealing with mass movement of persons departing Isla Del Sue.no on boats for the United States during 2005-2007. We used visual analytics to explore migration patterns, characterize the choice and evolution of landing sites, characterize the geographical patterns of interdictions and determine the successful landing rate. Our ComVis tool, in connection with some helper applications and Google Earth, allowed us to explore geo-temporal characteristics of the data set and answer the challenge questions. The ComVis project file captures the visual analysis context and facilitates better collaboration among team members.
Model-driven Visual Analytics
Authors: Supriya Garg, Julia Eunju Nam, I. V. Ramakrishnan, Klaus Mueller 0001
Published at VAST, 2008

We describe a visual analytics (VA) infrastructure, rooted on techniques in machine learning and logic-based deductive reasoning that will assist analysts to make sense of large, complex data sets by facilitating the generation and validation of models representing relationships in the data. We use logic programming (LP) as the underlying computing machinery to encode the relations as rules and facts and compute with them. A unique aspect of our approach is that the LP rules are automatically learned, using Inductive Logic Programming, from examples of data that the analyst deems interesting when viewing the data in the high-dimensional visualization interface. Using this system, analysts will be able to construct models of arbitrary relationships in the data, explore the data for scenarios that fit the model, refine the model if necessary, and query the model to automatically analyze incoming (future) data exhibiting the encoded relationships. In other words it will support both model-driven data exploration, as well as data-driven model evolution. More importantly, by basing the construction of models on techniques from machine learning and logic-based deduction, the VA process will be both flexible in terms of modeling arbitrary, user-driven relationships in the data as well as readily scale across different data domains.
Multidimensional visual analysis using cross-filtered views
Authors: Chris E. Weaver
Published at VAST, 2008

Analysis of multidimensional data often requires careful examination of relationships across dimensions. Coordinated multiple view approaches have become commonplace in visual analysis tools because they directly support expression of complex multidimensional queries using simple interactions. However, generating such tools remains difficult because of the need to map domain-specific data structures and semantics into the idiosyncratic combinations of interdependent data and visual abstractions needed to reveal particular patterns and distributions in cross-dimensional relationships. This paper describes: (1) a method for interactively expressing sequences of multidimensional set queries by cross-filtering data values across pairs of views, and (2) design strategies for constructing coordinated multiple view interfaces for cross-filtered visual analysis of multidimensional data sets. Using examples of cross-filtered visualizations of data from several different domains, we describe how cross-filtering can be modularized and reused across designs, flexibly customized with respect to data types across multiple dimensions, and incorporated into more wide-ranging multiple view designs. The demonstrated analytic utility of these examples suggest that cross-filtering is a suitable design pattern for instantiation in a wide variety of visual analysis tools.
Multivariate visual explanation for high dimensional datasets
Authors: Scott Barlowe, Tianyi Zhang, Yujie Liu, Jing Yang 0001, Donald J. Jacobs
Published at VAST, 2008

Understanding multivariate relationships is an important task in multivariate data analysis. Unfortunately, existing multivariate visualization systems lose effectiveness when analyzing relationships among variables that span more than a few dimensions. We present a novel multivariate visual explanation approach that helps users interactively discover multivariate relationships among a large number of dimensions by integrating automatic numerical differentiation techniques and multidimensional visualization techniques. The result is an efficient workflow for multivariate analysis model construction, interactive dimension reduction, and multivariate knowledge discovery leveraging both automatic multivariate analysis and interactive multivariate data visual exploration. Case studies and a formal user study with a real dataset illustrate the effectiveness of this approach.
Narratives: A visualization to track narrative events as they develop
Authors: Danyel Fisher, Aaron Hoff, George G. Robertson, Matthew Hurst
Published at VAST, 2008

Analyzing unstructured text streams can be challenging. One popular approach is to isolate specific themes in the text, and to visualize the connections between them. Some existing systems, like ThemeRiver, provide a temporal view of changes in themes; other systems, like In-Spire, use clustering techniques to help an analyst identify the themes at a single point in time. Narratives combines both of these techniques; it uses a temporal axis to visualize ways that concepts have changed over time, and introduces several methods to explore how those concepts relate to each other. Narratives is designed to help the user place news stories in their historical and social context by understanding how the major topics associated with them have changed over time. Users can relate articles through time by examining the topical keywords that summarize a specific news event. By tracking the attention to a news article in the form of references in social media (such as weblogs), a user discovers both important events and measures the social relevance of these stories.
Spatio-temporal aggregation for visual analysis of movements
Authors: Gennady L. Andrienko, Natalia V. Andrienko
Published at VAST, 2008

Data about movements of various objects are collected in growing amounts by means of current tracking technologies. Traditional approaches to visualization and interactive exploration of movement data cannot cope with data of such sizes. In this research paper we investigate the ways of using aggregation for visual analysis of movement data. We define aggregation methods suitable for movement data and find visualization and interaction techniques to represent results of aggregations and enable comprehensive exploration of the data. We consider two possible views of movement, traffic-oriented and trajectory-oriented. Each view requires different methods of analysis and of data aggregation. We illustrate our argument with example data resulting from tracking multiple cars in Milan and example analysis tasks from the domain of city traffic management.
Supporting exploration awareness for visual analytics
Authors: Yedendra Babu Shrinivasan, Jarke J. van Wijk
Published at VAST, 2008

While exploring data using information visualization, analysts try to make sense of the data, build cases, and present them to others. However, if the exploration is long or done in multiple sessions, it can be hard for analysts to remember all interesting visualizations and the relationships among them they have seen. Often, they will see the same or similar visualizations, and are unable to recall when, why and how they have seen something similar. Recalling and retrieving interesting visualizations are important tasks for the analysis processes such as problem solving, reasoning, and conceptualization. In this paper, we argue that offering support for thinking based on past analysis processes is important, and present a solution for this.
The Scalable Reasoning System: Lightweight visualization for distributed analytics
Authors: William A. Pike, Joe Bruce, Bob Baddeley, Daniel M. Best, Lyndsey Franklin, Richard May 0001, Douglas M. Rice, Roderick M. Riensche, Katarina Younkin
Published at VAST, 2008

A central challenge in visual analytics is the creation of accessible, widely distributable analysis applications that bring the benefits of visual discovery to as broad a user base as possible. Moreover, to support the role of visualization in the knowledge creation process, it is advantageous to allow users to describe the reasoning strategies they employ while interacting with analytic environments. We introduce an application suite called the scalable reasoning system (SRS), which provides Web-based and mobile interfaces for visual analysis. The service-oriented analytic framework that underlies SRS provides a platform for deploying pervasive visual analytic environments across an enterprise. SRS represents a ldquolightweightrdquo approach to visual analytics whereby thin client analytic applications can be rapidly deployed in a platform-agnostic fashion. Client applications support multiple coordinated views while giving analysts the ability to record evidence, assumptions, hypotheses and other reasoning artifacts. We describe the capabilities of SRS in the context of a real-world deployment at a regional law enforcement organization.
Understanding syndromic hotspots - a visual analytics approach
Authors: Ross Maciejewski, Stephen Rudolph, Ryan Hafen, Ahmad M. Abusalah, Mohamed Yakout, Mourad Ouzzani, William S. Cleveland, Shaun J. Grannis, Michael Wade, David S. Ebert
Published at VAST, 2008

When analyzing syndromic surveillance data, health care officials look for areas with unusually high cases of syndromes. Unfortunately, many outbreaks are difficult to detect because their signal is obscured by the statistical noise. Consequently, many detection algorithms have a high false positive rate. While many false alerts can be easily filtered by trained epidemiologists, others require health officials to drill down into the data, analyzing specific segments of the population and historical trends over time and space. Furthermore, the ability to accurately recognize meaningful patterns in the data becomes more challenging as these data sources increase in volume and complexity. To facilitate more accurate and efficient event detection, we have created a visual analytics tool that provides analysts with linked geo-spatiotemporal and statistical analytic views. We model syndromic hotspots by applying a kernel density estimation on the population sample. When an analyst selects a syndromic hotspot, temporal statistical graphs of the hotspot are created. Similarly, regions in the statistical plots may be selected to generate geospatial features specific to the current time period. Demographic filtering can then be combined to determine if certain populations are more affected than others. These tools allow analysts to perform real-time hypothesis testing and evaluation.
Using SocialAction to uncover structure in social networks over time
Authors: Adam Perer
Published at VAST, 2008

I describe how SocialAction was used to find insights in an evolving social structure VAST Challenge 2008psilas Mini-Challenge 3. This analysis and SocialAction were given the award, ldquoCell Phone Mini Challenge Award: Time Visualizations of Cell Phone Activityrdquo.
Using visual analytics to maintain situation awareness in astrophysics
Authors: Cecilia R. Aragon, Sarah S. Poon, Gregory S. Aldering, Rollin C. Thomas, Robert Quimby
Published at VAST, 2008

We present a novel collaborative visual analytics application for cognitively overloaded users in the astrophysics domain. The system was developed for scientists needing to analyze heterogeneous, complex data under time pressure, and then make predictions and time-critical decisions rapidly and correctly under a constant influx of changing data. The Sunfall Data Taking system utilizes several novel visualization and analysis techniques to enable a team of geographically distributed domain specialists to effectively and remotely maneuver a custom-built instrument under challenging operational conditions. Sunfall Data Taking has been in use for over eighteen months by a major international astrophysics collaboration (the largest data volume supernova search currently in operation), and has substantially improved the operational efficiency of its users. We describe the system design process by an interdisciplinary team, the system architecture, and the results of an informal usability evaluation of the production system by domain experts in the context of Endsleypsilas three levels of situation awareness.
VAST 2008 Challenge: Introducing mini-challenges
Authors: Georges G. Grinstein, Catherine Plaisant, Sharon J. Laskowski, Teresa O'Connell, Jean Scholtz, Mark A. Whiting
Published at VAST, 2008

Visual analytics experts realize that one effective way to push the field forward and to develop metrics for measuring the performance of various visual analytics components is to hold an annual competition. The VAST 2008 Challenge is the third year that such a competition was held in conjunction with the IEEE Visual Analytics Science and Technology (VAST) symposium. The authors restructured the contest format used in 2006 and 2007 to reduce the barriers to participation and offered four mini-challenges and a Grand Challenge. Mini Challenge participants were to use visual analytic tools to explore one of four heterogeneous data collections to analyze specific activities of a fictitious, controversial movement. Questions asked in the Grand Challenge required the participants to synthesize data from all four data sets. In this paper we give a brief overview of the data sets, the tasks, the participation, the judging, and the results.
Visual analysis for mutual fund performance
Authors: Ye Zhao 0003, Jamal Alsakran, Xinlei Zhao
Published at VAST, 2008

Mutual funds are one of the most important investment instruments available. However, choosing among mutual funds is not an easy task because they vary in many different dimensions, such as asset size, turnover and fee structure, and these characteristics may affect fund returns. It is thus important to understand the relation between fund performance and these properties. In this work, we use a new visual analytical tool, the density-based distribution map, to assist in this task. By visualizing various important fund characteristics from a real-world database of the US stock funds, our new visual representations greatly help understand the relation between fund characteristics and returns.
Visual analysis of seismic simulation data
Authors: Florian Jürgen Gerhardt, Jörg Meyer 0002
Published at VAST, 2008

Seismic simulations use finite element methods to describe ground motion. The results of such numerical simulations are often difficult to interpret for decision makers. We describe a terrain rendering engine that uses photorealistic metaphors to represent typical terrain properties without representing an actual terrain. In the context of ground motion, a simulation of the effects of various types of earthquakes on buildings has been conducted. Usually, such structural response simulations are carried out independently and are being visualized separate from the ground motion simulation. We combine the results from both simulations in an interactive, hybrid visualization so that decision makers (first responders and emergency management agencies) are provided with a photo-realistic, simulated view of various earthquake scenarios, enabling them to study the effect of various earthquakes on buildings typical for a rural or urban area. We present a method for visually analyzing large-scale simulation data from different sources (ground motion simulation and structural response simulation) using photorealistic metaphors. We have implemented an intuitive, interactive system for visual analysis and inspection of possible effects of various types of earthquakes on an inventory of buildings typical for a particular area. The underlying rendering system can be easily adapted for other simulations, such as smoke plumes or biohazards.
Visual analytics for complex concepts using a human cognition model
Authors: Tera Marie Green, William Ribarsky, Brian D. Fisher
Published at VAST, 2008

As the information being visualized and the process of understanding that information both become increasingly complex, it is necessary to develop new visualization approaches that facilitate the flow of human reasoning. In this paper, we endeavor to push visualization design a step beyond current user models by discussing a modeling framework of human ldquohigher cognition.rdquo Based on this cognition model, we present design guidelines for the development of visual interfaces designed to maximize the complementary cognitive strengths of both human and computer. Some of these principles are already being reflected in the better visual analytics designs, while others have not yet been applied or fully applied. But none of the guidelines have explained the deeper rationale that the model provides. Lastly, we discuss and assess these visual analytics guidelines through the evaluation of several visualization examples.
Visual cluster analysis of trajectory data with interactive Kohonen Maps
Authors: Tobias Schreck, Jürgen Bernard, Tatiana von Landesberger, Jörn Kohlhammer
Published at VAST, 2008

Visual-interactive cluster analysis provides valuable tools for effectively analyzing large and complex data sets. Due to desirable properties and an inherent predisposition for visualization, the Kohonen Feature Map (or self-organizing map, or SOM) algorithm is among the most popular and widely used visual clustering techniques. However, the unsupervised nature of the algorithm may be disadvantageous in certain applications. Depending on initialization and data characteristics, cluster maps (cluster layouts) may emerge that do not comply with user preferences, expectations, or the application context. Considering SOM-based analysis of trajectory data, we propose a comprehensive visual-interactive monitoring and control framework extending the basic SOM algorithm. The framework implements the general Visual Analytics idea to effectively combine automatic data analysis with human expert supervision. It provides simple, yet effective facilities for visually monitoring and interactively controlling the trajectory clustering process at arbitrary levels of detail. The approach allows the user to leverage existing domain knowledge and user preferences, arriving at improved cluster maps. We apply the framework on a trajectory clustering problem, demonstrating its potential in combining both unsupervised (machine) and supervised (human expert) processing, in producing appropriate cluster results.
Visual evaluation of text features for document summarization and analysis
Authors: Daniela Oelke, Peter Bak, Daniel A. Keim, Mark Last, Guy Danon
Published at VAST, 2008

Thanks to the Web-related and other advanced technologies, textual information is increasingly being stored in digital form and posted online. Automatic methods to analyze such textual information are becoming inevitable. Many of those methods are based on quantitative text features. Analysts face the challenge to choose the most appropriate features for their tasks. This requires effective approaches for evaluation and feature-engineering.