IEEE VIS Publication Dataset

Cellular biology deals with studying the behavior of cells. Current time-lapse imaging microscopes help us capture the progress of experiments at intervals that allow for understanding of the dynamic and kinematic behavior of the cells. On the other hand, these devices generate such massive amounts of data (250GB of data per experiment) that manual sieving of data to identify interesting patterns becomes virtually impossible. In this paper we propose an end-to-end system to analyze time-lapse images of the cultures of human neural stem cells (hNSC), that includes an image processing system to analyze the images to extract all the relevant geometric and statistical features within and between images, a database management system to manage and handle queries on the data, a visual analytic system to navigate through the data, and a visual query system to explore different relationships and correlations between the parameters. In each stage of the pipeline we make novel algorithmic and conceptual contributions, and the entire system design is motivated by many different yet unanswered exploratory questions pursued by our neurobiologist collaborators. With a few examples we show how such abstract biological queries can be analyzed and answered by our system.

To visualize security trends for the data set provided by the VAST 2011 Mini Challenge #2 a custom tool has been developed. Open source tools [1,2], web programming languages [4,7] and an open source database [3] has been used to work with the data and create a visualization for security log files containing network security trends. In this paper, the tools and methods used for the analysis are described. The methods include the log synchronization with different timezone and the development of heat maps and parallel coordinates charts. To develop the visualization, Processing and Canvas [4,7] was used.

To make decisions about the long-term preservation and access of large digital collections, archivists gather information such as the collections' contents, their organizational structure, and their file format composition. To date, the process of analyzing a collection - from data gathering to exploratory analysis and final conclusions - has largely been conducted using pen and paper methods. To help archivists analyze large-scale digital collections for archival purposes, we developed an interactive visual analytics application. The application narrows down different kinds of information about the collection, and presents them as meaningful data views. Multiple views and analysis features can be linked or unlinked on demand to enable researchers to compare and contrast different analyses, and to identify trends. We describe and present two user scenarios to show how the application allowed archivists to learn about a collection with accuracy, facilitated decision-making, and helped them arrive at conclusions.

Analyst's Workspace is a sensemaking environment designed specifically for use of large, high-resolution displays. It employs a spatial workspace to integrate foraging and synthesis activities into a unified process. In this paper we describe how Analyst's Workspace solved the VAST 2011 mini-challenge #3 and discuss some of the unique features of the environment.

Recent work in visual analytics has explored the extent to which information regarding analyst action and reasoning can be inferred from interaction. However, these methods typically rely on humans instead of automatic extraction techniques. Furthermore, there is little discussion regarding the role of user frustration when interacting with a visual interface. We demonstrate that automatic extraction of user frustration is possible given action-level visualization interaction logs. An experiment is described which collects data that accurately reflects user emotion transitions and corresponding interaction sequences. This data is then used in building HiddenMarkov Models (HMMs) which statistically connect interaction events with frustration. The capabilities of HMMs in predicting user frustration are tested using standard machine learning evaluation methods. The resulting classifier serves as a suitable predictor of user frustration that performs similarly across different users and datasets.

This paper studies the interpretability of transformations of labeled higher dimensional data into a 2D representation (scatterplots) for visual classification.1In this context, the term interpretability has two components: the interpretability of the visualization (the image itself) and the interpretability of the visualization axes (the data transformation functions). We define a data transformation function as any linear or non-linear function of the original variables mapping the data into 1D. Even for a small dataset, the space of possible data transformations is beyond the limit of manual exploration, therefore it is important to develop automated techniques that capture both aspects of interpretability so that they can be used to guide the search process without human intervention. The goal of the search process is to find a smaller number of interpretable data transformations for the users to explore. We briefly discuss how we used such automated measures in an evolutionary computing based data dimensionality reduction application for visual analytics. In this paper, we present a two-part user study in which we separately investigated how humans rated the visualizations of labeled data and comprehensibility of mathematical expressions that could be used as data transformation functions. In the first part, we compared human perception with a number of automated measures from the machine learning and visual analytics literature. In the second part, we studied how various structural properties of an expression related to its interpretability.

We present a system for the interactive construction and analysis of decision trees that enables domain experts to bring in domain specific knowledge. We identify different user tasks and corresponding requirements, and develop a system incorporating a tight integration of visualization, interaction and algorithmic support. Domain experts are supported in growing, pruning, optimizing and analysing decision trees. Furthermore, we present a scalable decision tree visualization optimized for exploration. We show the effectiveness of our approach by applying the methods to two use cases. The first case illustrates the advantages of interactive construction, the second case demonstrates the effectiveness of analysis of decision trees and exploration of the structure of the data.

While intelligence analysis has been a primary target domain for visual analytics system development, relatively little user and task analysis has been conducted within this area. Our research community's understanding of the work processes and practices of intelligence analysts is not deep enough to adequately address their needs. Without a better understanding of the analysts and their problems, we cannot build visual analytics systems that integrate well with their work processes and truly provide benefit to them. In order to close this knowledge gap, we conducted a longitudinal, observational field study of intelligence analysts in training within the intelligence program at Mercyhurst College. We observed three teams of analysts, each working on an intelligence problem for a ten-week period. Based upon study findings, we describe and characterize processes and methods of intelligence analysis that we observed, make clarifications regarding the processes and practices, and suggest design implications for visual analytics systems for intelligence analysis.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.