DevLan/vispubs huggingface dataset:
City sentinel - VAST 2011 mini challenge 1 award: "Outstanding integration of computational and visual methods"
Authors: Norbert Bánfi, László Dudás, Zsolt Fekete, Julianna Göbölös-Szabó, András Lukács, Ádám Nagy, Adrienn Szabó, Zoltán Szabó 0002, Gábor Szücs
Published at VAST, 2011

We present City Sentinel, an in-house built visual analytic software capable of handling a large collection of textual documents by combining diverse text mining and visualization tools. We applied this tool for the Vast Challenge 2011, Mini Challenge 1 over millions of tweet messages. We demonstrate how City Sentinel aided the analyst in retrieving the hidden information from the tweet messages to analyze and locate a hypothetical epidemic outbreak.
epSpread - Storyboarding for visual analytics
Authors: Llyr ap Cenydd, Rick Walker, Serban R. Pop, Helen C. Miles, Chris J. Hughes, William John Teahan, Jonathan Roberts 0002
Published at VAST, 2011

We present epSpread, an analysis and storyboarding tool for geolocated microblogging data. Individual time points and ranges are analysed through queries, heatmaps, word clouds and streamgraphs. The underlying narrative is shown on a storyboard-style timeline for discussion, refinement and presentation. The tool was used to analyse data from the VAST Challenge 2011 Mini-Challenge 1, tracking the spread of an epidemic using microblogging data. In this article we describe how the tool was used to identify the origin and track the spread of the epidemic.
Evaluation of large display interaction using smart phones
Authors: Jens Bauer, Sebastian Thelen, Achim Ebert
Published at VAST, 2011

Visual analytics, “the science of analytical reasoning facilitated by visual interactive interfaces” [5], puts high demands on the applications visualization as well as interaction capabilities. Due to their size large high-resolution screens have become popular display devices, especially when used in collaborative data analysis scenarios. However, traditional interaction methods based on combinations of computer mice and keyboards often do not scale to the number of users or the size of the display. Modern smart phones featuring multi-modal input/output and considerable memory offer a way to address these issues. In the last couple of years they have become common everyday life gadgets. In this paper we conduct an extensive user study comparing the experience of test candidates when using traditional input devices and metaphors with the one when using new smart phone based techniques, like multi-modal drag and tilt. Candidates were asked to complete various interaction tasks relevant for most applications on a large, monitor-based, high-resolution tiled wall system. Our study evaluates both user performance and satisfaction, identifying strengths and weaknesses of the researched interaction methods in specific tasks. Results reveal good performance of users in certain tasks when using the new interaction techniques. Even first-time users were able to complete a task faster with the smart phone than with traditional devices.
Exploring agent-based simulations using temporal graphs
Authors: R. Jordan Crouser, Jeremy G. Freeman, Remco Chang
Published at VAST, 2011

Agent-based simulation has become a key technique for modeling and simulating dynamic, complicated behaviors in social and behavioral sciences. Lacking the appropriate tools and support, it is difficult for social scientists to thoroughly analyze the results of these simulations. In this work, we capture the complex relationships between discrete simulation states by visualizing the data as a temporal graph. In collaboration with expert analysts, we identify two graph structures which capture important relationships between pivotal states in the simulation and their inevitable outcomes. Finally, we demonstrate the utility of these structures in the interactive analysis of a large-scale social science simulation of political power in present-day Thailand.
Exploring proportions: Comparative visualization of categorical data
Authors: Harald Piringer, Matthias Buchetics
Published at VAST, 2011

This poster describes an approach to facilitate comparisons in multi-dimensional categorical data. The key idea is to represent over- or under-proportional relationships explicitly. On an overview level, the visualization of various measures conveys pair-wise relationships between categorical dimensions. For more details, interaction supports to relate a single category to all categories of multiple dimensions. We discuss methods for representing relationships and visualization-driven strategies for ordering dimensions and categories, and we illustrate the approach by means of data from a social survey.
Find distance function, hide model inference
Authors: Jingjing Liu, Eli T. Brown, Remco Chang
Published at VAST, 2011

Faced with a large, high-dimensional dataset, many turn to data analysis approaches that they understand less well than the domain of their data. An expert's knowledge can be leveraged into many types of analysis via a domain-specific distance function, but creating such a function is not intuitive to do by hand. We have created a system that shows an initial visualization, adapts to user feedback, and produces a distance function as a result. Specifically, we present a multidimensional scaling (MDS) visualization and an iterative feedback mechanism for a user to affect the distance function that informs the visualization without having to adjust the parameters of the visualization directly. An encouraging experimental result suggests that using this tool, data attributes with useless data are given low importance in the distance function.
From movement tracks through events to places: Extracting and characterizing significant places from mobility data
Authors: Gennady L. Andrienko, Natalia V. Andrienko, Christophe Hurter, Salvatore Rinzivillo, Stefan Wrobel
Published at VAST, 2011

We propose a visual analytics procedure for analyzing movement data, i.e., recorded tracks of moving objects. It is oriented to a class of problems where it is required to determine significant places on the basis of certain types of events occurring repeatedly in movement data. The procedure consists of four major steps: (1) event extraction from trajectories; (2) event clustering and extraction of relevant places; (3) spatio-temporal aggregation of events or trajectories; (4) analysis of the aggregated data. All steps are scalable with respect to the amount of the data under analysis. We demonstrate the use of the procedure by example of two real-world problems requiring analysis at different spatial scales.
G-PARE: A visual analytic tool for comparative analysis of uncertain graphs
Authors: Hossam Sharara, Awalin Sopan, Galileo Namata, Lise Getoor, Lisa Singh
Published at VAST, 2011

There are a growing number of machine learning algorithms which operate on graphs. Example applications for these algorithms include predicting which customers will recommend products to their friends in a viral marketing campaign using a customer network, predicting the topics of publications in a citation network, or predicting the political affiliations of people in a social network. It is important for an analyst to have tools to help compare the output of these machine learning algorithms. In this work, we present G-PARE, a visual analytic tool for comparing two uncertain graphs, where each uncertain graph is produced by a machine learning algorithm which outputs probabilities over node labels. G-PARE provides several different views which allow users to obtain a global overview of the algorithms output, as well as focused views that show subsets of nodes of interest. By providing an adaptive exploration environment, G-PARE guides the users to places in the graph where two algorithms predictions agree and places where they disagree. This enables the user to follow cascades of misclassifications by comparing the algorithms outcome with the ground truth. After describing the features of G-PARE, we illustrate its utility through several use cases based on networks from different domains.
Geovisual analytics for cyber security: Adopting the GeoViz Toolkit
Authors: Nicklaus A. Giacobe, Sen Xu
Published at VAST, 2011

For the VAST 2011 Network Security Mini-Challenge, we adopted geovisual analytic methods and applied them in the field of network security. We used the GeoViz Toolkit [1] to represent cyber security events, by fabricating a simple “geography” of several sets of blocks (one for the workstations, one for the servers, and one for the Internet) using ArcGIS 10 (by ESRI - Environmental System Research Institute). Security data was tabulated using Perl scripts to parse the logs in order to create representations of event frequency and where they occurred on the network. The tabulated security data was then added as attributes of the geography. Exploration of the data and subsequent analysis of the meaning and impact of the cyber security events was made possible using the GeoViz Toolkit.
Guiding feature subset selection with an interactive visualization
Authors: Thorsten May, Andreas Bannach, James Davey, Tobias Ruppert, Jörn Kohlhammer
Published at VAST, 2011

We propose a method for the semi-automated refinement of the results of feature subset selection algorithms. Feature subset selection is a preliminary step in data analysis which identifies the most useful subset of features (columns) in a data table. So-called filter techniques use statistical ranking measures for the correlation of features. Usually a measure is applied to all entities (rows) of a data table. However, the differing contributions of subsets of data entities are masked by statistical aggregation. Feature and entity subset selection are, thus, highly interdependent. Due to the difficulty in visualizing a high-dimensional data table, most feature subset selection algorithms are applied as a black box at the outset of an analysis. Our visualization technique, SmartStripes, allows users to step into the feature subset selection process. It enables the investigation of dependencies and interdependencies between different feature and entity subsets. A user may even choose to control the iterations manually, taking into account the ranking measures, the contributions of different entity subsets, as well as the semantics of the features.
Guiding security analysis through visualization
Authors: Lane Harrison, Wenwen Dou, Aidong Lu, William Ribarsky, Derek Xiaoyu Wang
Published at VAST, 2011

We present a multiple views visualization for the security data in the VAST 2010 Mini Challenge 2. The visualization is used to monitor log event activity on the network log data included in the challenge. Interactions are provided that allow analysts to investigate suspicious activity and escalate events as needed. Additionally, a database application is used to allow SQL queries for more detailed investigation.
How locus of control influences compatibility with visualization style
Authors: Caroline Ziemkiewicz, R. Jordan Crouser, Ashley Rye Yauilla, Sara L. Su, William Ribarsky, Remco Chang
Published at VAST, 2011

Existing research suggests that individual personality differences are correlated with a user's speed and accuracy in solving problems with different types of complex visualization systems. In this paper, we extend this research by isolating factors in personality traits as well as in the visualizations that could have contributed to the observed correlation. We focus on a personality trait known as “locus of control,” which represents a person's tendency to see themselves as controlled by or in control of external events. To isolate variables of the visualization design, we control extraneous factors such as color, interaction, and labeling, and specifically focus on the overall layout style of the visualizations. We conduct a user study with four visualizations that gradually shift from an indentation metaphor to a containment metaphor and compare the participants' speed, accuracy, and preference with their locus of control. Our findings demonstrate that there is indeed a correlation between the two: participants with an internal locus of control perform more poorly with visualizations that employ a containment metaphor, while those with an external locus of control perform well with such visualizations. We discuss a possible explanation for this relationship based in cognitive psychology and propose that these results can be used to better understand how people use visualizations and how to adapt visual analytics design to an individual user's needs.
Interactive data analysis with nSpace2(c)
Authors: Casey M. Canfield, David Sheffield
Published at VAST, 2011

nSpace2 is an innovative visual analytics tool that was the primary platform used to search, evaluate, and organize the data in the VAST 2011 Mini Challenge #3 dataset. nSpace2 is a web-based tool that is designed to facilitate the back-and-forth flow of the multiple steps of an analysis process, including search, data triage, organization, sense-making, and reporting. This paper describes how nSpace2 was used to assist every step of the analysis process for this VAST challenge.
Interactive decision making using dissimilarity to visually represented prototypes
Authors: Gosia Migut, Jan C. van Gemert, Marcel Worring
Published at VAST, 2011

To make informed decisions, an expert has to reason with multi-dimensional, heterogeneous data and analysis results of these. Items in such datasets are typically represented by features. However, as argued in cognitive science, features do not yield an optimal space for human reasoning. In fact, humans tend to organize complex information in terms of prototypes or known cases rather than in absolute terms. When confronted with unknown data items, humans assess them in terms of similarity to these prototypical elements. Interestingly, an analogues similarity-to-prototype approach, where prototypes are taken from the data, has been successfully applied in machine learning. Combining such a machine learning approach with human prototypical reasoning in a Visual Analytics context requires to integrate similarity-based classification with interactive visualizations. To that end, the data prototypes should be visually represented to trigger direct associations to cases familiar to the domain experts. In this paper, we propose a set of highly interactive visualizations to explore data and classification results in terms of dissimilarities to visually represented prototypes. We argue that this approach not only supports human reasoning processes, but is also suitable to enhance understanding of heterogeneous data. The proposed framework is applied to a risk assessment case study in Forensic Psychiatry.
Interactive visual comparison of multiple trees
Authors: Sebastian Bremm, Tatiana von Landesberger, Martin Hess, Tobias Schreck, Philipp Weil, Kay Hamacher
Published at VAST, 2011

Traditionally, the visual analysis of hierarchies, respectively, trees, is conducted by focusing on one given hierarchy. However, in many research areas multiple, differing hierarchies need to be analyzed simultaneously in a comparative way - in particular to highlight differences between them, which sometimes can be subtle. A prominent example is the analysis of so-called phylogenetic trees in biology, reflecting hierarchical evolutionary relationships among a set of organisms. Typically, the analysis considers multiple phylogenetic trees, either to account for statistical significance or for differences in derivation of such evolutionary hierarchies; for example, based on different input data, such as the 16S ribosomal RNA and protein sequences of highly conserved enzymes. The simultaneous analysis of a collection of such trees leads to more insight into the evolutionary process. We introduce a novel visual analytics approach for the comparison of multiple hierarchies focusing on both global and local structures. A new tree comparison score has been elaborated for the identification of interesting patterns. We developed a set of linked hierarchy views showing the results of automatic tree comparison on various levels of details. This combined approach offers detailed assessment of local and global tree similarities. The approach was developed in close cooperation with experts from the evolutionary biology domain. We apply it to a phylogenetic data set on bacterial ancestry, demonstrating its application benefit.
Jigsaw to save vastopolis
Authors: Elizabeth Braunstein, Carsten Görg, Zhicheng Liu, John T. Stasko
Published at VAST, 2011

This article describes our analytic process and experience of using the Jigsaw system in working on the VAST 2011 Mini Challenge 3. We describe how we extracted and worked with entities from the documents, and how Jigsaw's computational analysis capabilities and visualizations scaffolded the investigation. Based on our experiences, we discuss desirable features that would enhance the analytic power of Jigsaw.
KD-photomap: Exploring photographs in space and time
Authors: Iulian Peca, Haolin Zhi, Katerina Vrotsou, Natalia V. Andrienko, Gennady L. Andrienko
Published at VAST, 2011

KD-photomap is a web-based visual analytics system for browsing collections of geotagged Flickr photographs in search of interesting pictures, places, and events. Spatial filtering of the data is performed through zooming, moving or searching along the map. Temporal filtering is possible through defining time windows using interactive histograms and calendar controls. Information about the number and spatiotemporal distribution of photos captured in an explored area is continuously provided using various visual cues.
Mapping an epidemic outbreak: Effective analysis and presentation
Authors: Kevin Boone, Edward Swing
Published at VAST, 2011

The microblog challenge presented an opportunity to use commercial software for visual analysis. An epidemic outbreak occurred in the city of Vastopolis, requiring visualizations of symptoms and their spread over time. Using these tools, analysts could successfully identify the outbreak's origin and pattern of dispersion. The maps used to analyze the data and present the results provided clear, easily understood representations, and presented a logical explanation of a complex progression of events.
MobileAnalymator: Animating data changes on mobile devices
Authors: Victor Y. Chen, Cheryl Z. Qian, Li Zhang
Published at VAST, 2011

MobileAnalymator (Mobile Analysis Animator) is a visual analytic system designed to analyze geospatial-temporal data on mobile devices. The system is an Internet based application that allows analysts to work in flexile enviornments at anytime. Its client side is developed by Adobe Flash to animate and interact with data. The server side uses Java and MySQL to query, compute, and serve data. The analyst can run the analytical task from a tablet (or computer) with Internet connection. MobileAnalymator adopted spatial and temporal autocorrelations in the interface design and integrated tangible interaction in the navigation to support analysis process.
Network-based visual analysis of tabular data
Authors: Zhicheng Liu, Shamkant B. Navathe, John T. Stasko
Published at VAST, 2011

Tabular data are pervasive. Although tables often describe multivariate data without explicit network semantics, it may be advantageous to explore the data modeled as a graph or network for analysis. Even when a given table design conveys some static network semantics, analysts may want to look at multiple networks from different perspectives, at different levels of abstraction, and with different edge semantics. We present a system called Ploceus that offers a general approach for performing multi-dimensional and multi-level network-based visual analysis on multivariate tabular data. Powered by an underlying relational algebraic framework, Ploceus supports flexible construction and transformation of networks through a direct manipulation interface, and integrates dynamic network manipulation with visual exploration for a seamless analytic experience.
Observation-level interaction with statistical models for visual analytics
Authors: Alex Endert, Chao Han, Dipayan Maiti, Leanna House, Scotland Leman, Chris North 0001
Published at VAST, 2011

In visual analytics, sensemaking is facilitated through interactive visual exploration of data. Throughout this dynamic process, users combine their domain knowledge with the dataset to create insight. Therefore, visual analytic tools exist that aid sensemaking by providing various interaction techniques that focus on allowing users to change the visual representation through adjusting parameters of the underlying statistical model. However, we postulate that the process of sensemaking is not focused on a series of parameter adjustments, but instead, a series of perceived connections and patterns within the data. Thus, how can models for visual analytic tools be designed, so that users can express their reasoning on observations (the data), instead of directly on the model or tunable parameters? Observation level (and thus “observation”) in this paper refers to the data points within a visualization. In this paper, we explore two possible observation-level interactions, namely exploratory and expressive, within the context of three statistical methods, Probabilistic Principal Component Analysis (PPCA), Multidimensional Scaling (MDS), and Generative Topographic Mapping (GTM). We discuss the importance of these two types of observation level interactions, in terms of how they occur within the sensemaking process. Further, we present use cases for GTM, MDS, and PPCA, illustrating how observation level interaction can be incorporated into visual analytic tools.
Obvious: A meta-toolkit to encapsulate information visualization toolkits - One toolkit to bind them all
Authors: Jean-Daniel Fekete, Pierre-Luc Hemery, Thomas Baudel, Jo Wood
Published at VAST, 2011

This article describes “Obvious”: a meta-toolkit that abstracts and encapsulates information visualization toolkits implemented in the Java language. It intends to unify their use and postpone the choice of which concrete toolkit(s) to use later-on in the development of visual analytics applications. We also report on the lessons we have learned when wrapping popular toolkits with Obvious, namely Prefuse, the InfoVis Toolkit, partly Improvise, JUNG and other data management libraries. We show several examples on the uses of Obvious, how the different toolkits can be combined, for instance sharing their data models. We also show how Weka and Rapid-Miner, two popular machine-learning toolkits, have been wrapped with Obvious and can be used directly with all the other wrapped toolkits. We expect Obvious to start a co-evolution process: Obvious is meant to evolve when more components of Information Visualization systems will become consensual. It is also designed to help information visualization systems adhere to the best practices to provide a higher level of interoperability and leverage the domain of visual analytics.