Kristin A. Cook

A visual analytics agenda

Researchers have made significant progress in disciplines such as scientific and information visualization, statistically based exploratory and confirmatory analysis, data and knowledge representations, and perceptual and cognitive sciences. Although some research is being done in this area, the pace at which new technologies and technical talents are becoming available is far too slow to meet the urgent need. National Visualization and Analytics Centers goal is to advance the state of the science to enable analysts to detect the expected and discover the unexpected from massive and dynamic information streams and databases consisting of data of multiple types and from multiple sources, even though the data are often conflicting and incomplete. Visual analytics is a multidisciplinary field that includes the following focus areas: (i) analytical reasoning techniques, (ii) visual representations and interaction techniques, (iii) data representations and transformations, (iv) techniques to support production, presentation, and dissemination of analytical results. The R&D agenda for visual analytics addresses technical needs for each of these focus areas, as well as recommendations for speeding the movement of promising technologies into practice. This article provides only the concise summary of the R&D agenda. We encourage reading, discussion, and debate as well as active innovation toward the agenda for visual analysis.

Guest Editors' Introduction: Discovering the Unexpected

The marriage of computation, visual representation, and interactive thinking supports intensive analysis. The goal is not only to permit users to detect expected events, such as might be predicted by models, but also to help users discover the unexpected—the surprising anomalies, changes, patterns, and relationships that are then examined and assessed to develop new insight. The Guest Editors discuss the key issues and challenges associated with discovering the unexpected, as well as introduce the articles that make up this Special Issue.

VAST Challenge 2012: Visual analytics for big data

The 2012 Visual Analytics Science and Technology (VAST) Challenge posed two challenge problems for participants to solve using a combination of visual analytics software and their own analytic reasoning abilities. Challenge 1 (C1) involved visualizing the network health of the fictitious Bank of Money to provide situation awareness and identify emerging trends that could signify network issues. Challenge 2 (C2) involved identifying the issues of concern within a region of the Bank of Money network experiencing operational difficulties utilizing the provided network logs. Participants were asked to analyze the data and provide solutions and explanations for both challenges. The data sets were downloaded by nearly 1100 people by the close of submissions. The VAST Challenge received 40 submissions with participants from 12 different countries, and 14 awards were given.

The VAST Challenge: history, scope, and outcomes: An introduction to the Special Issue

The annual Visual Analytics Science and Technology (VAST) challenge provides Visual Analytics researchers, developers, and designers an opportunity to apply their best tools and techniques against invented problems that include a realistic scenario, data, tasks, and questions to be answered. Submissions are processed much like conference papers, contestants are provided reviewer feedback, and excellence is recognized with awards. A day-long VAST Challenge workshop takes place each year at the IEEE VAST conference to share results and recognize outstanding submissions. Short papers are published each year in the annual VAST proceedings. Over the history of the challenge, participants have investigated a wide variety of scenarios, such as bioterrorism, epidemics, arms smuggling, social unrest, and computer network attacks, among many others. Contestants have been provided with large numbers of realistic but synthetic Coast Guard interdiction records, intelligence reports, hospitalization records, microblog records, personal RFID tag locations, huge amounts of cyber security log data, and several hours of video. This paper describes the process for developing the synthetic VAST Challenge datasets and conducting the annual challenges. This paper also provides an introduction to this special issue of Information Visualization, focusing on the impacts of the VAST Challenge.

Mixed-initiative visual analytics using task-driven recommendations

Visual data analysis is composed of a collection of cognitive actions and tasks to decompose, internalize, and recombine data to produce knowledge and insight. Visual analytic tools provide interactive visual interfaces to data to support discovery and sensemaking tasks, including forming hypotheses, asking questions, and evaluating and organizing evidence. Myriad analytic models can be incorporated into visual analytic systems at the cost of increasing complexity in the analytic discourse between user and system. Techniques exist to increase the usability of interacting with analytic models, such as inferring data models from user interactions to steer the underlying models of the system via semantic interaction, shielding users from having to do so explicitly. Such approaches are often also referred to as mixed-initiative systems. Sensemaking researchers have called for development of tools that facilitate analytic sensemaking through a combination of human and automated activities. However, design guidelines do not exist for mixed-initiative visual analytic systems to support iterative sensemaking. In this paper, we present candidate design guidelines and introduce the Active Data Environment (ADE) prototype, a spatial workspace supporting the analytic process via task recommendations invoked by inferences about user interactions within the workspace. ADE recommends data and relationships based on a task model, enabling users to co-reason with the system about their data in a single, spatial workspace. This paper provides an illustrative use case, a technical description of ADE, and a discussion of the strengths and limitations of the approach.

A multi-level middle-out cross-zooming approach for large graph analytics

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.

Visual analytics technology transition progress

The authors provide a description of the transition process for visual analytic tools and contrast this with the transition process for more traditional software tools. This paper takes this difference into account and describes a user-oriented approach to technology transition including a discussion of key factors that should be considered and adapted to each situation. The progress made in transitioning visual analytic tools in the past 5 years is described and challenges that remain are enumerated.

Typograph: Multiscale spatial exploration of text documents

Visualizing large document collections using a spatial layout of terms can enable quick overviews of information. These visual metaphors (e.g., word clouds, tag clouds, etc.) traditionally show a series of terms organized by space-filling algorithms. However, often lacking in these views is the ability to interactively explore the information to gain more detail, and the location and rendering of the terms are often not based on mathematical models that maintain relative distances from other information based on similarity metrics. In this paper, we present Typograph, a multi-scale spatial exploration visualization for large document collections. Based on the term-based visualization methods, Typograh enables multiple levels of detail (terms, phrases, snippets, and full documents) within the single spatialization. Further, the information is placed based on their relative similarity to other information to create the “near = similar” geographic metaphor. This paper discusses the design principles and functionality of Typograph and presents a use case analyzing Wikipedia to demonstrate usage.

Building Adoption of Visual Analytics Software

The impact of visual analytic software can only be fully realized if attention is focused on the development of approaches to facilitate broad adoption. While all technology adoption efforts face obstacles, the highly visual and interactive nature of visual analytics software as a cognitive aid poses particular technological and cultural challenges that must be addressed. Successful adoption requires different techniques at every phase of the technology adoption life cycle, from the innovators and the visionary early adopters to the more pragmatic early majority and finally to the less technologically-oriented late majority. This chapter provides an overview of the technology adoption life cycle and describes the particular challenges of technology adoption for visual analytics software. A case study of visual analytics technology adoption is considered, and the role of organizational culture is examined. Finally, an extensive set of guidelines is presented for facilitating visual analytics software adoption throughout the entire technology adoption life cycle.

VAST Challenge 2015: Mayhem at dinofun world

A fictitious amusement park and a larger-than-life hometown football hero provided participants in the VAST Challenge 2015 with an engaging yet complex storyline and setting in which to analyze movement and communication patterns. The datasets for the 2015 challenge were large—averaging nearly 10 million records per day over a three day period—with a simple straightforward structured format. The simplicity of the format belied a complex wealth of features contained in the data that needed to be discovered and understood to solve the tasks and questions that were posed. Two Mini-Challenges and a Grand Challenge compose the 2015 competition. Mini-Challenge 1 contained structured location and date-time data for park visitors, against which participants were to discern groups and their activities. Mini-Challenge 2 contained structured communication data consisting of metadata about time-stamped text messages sent between park visitors. The Grand Challenge required participants to use both movement and communication data to hypothesize when a crime was committed and identify the most likely suspects from all the park visitors. The VAST Challenge 2015 received 71 submissions, and the datasets were downloaded, at least partially, from 26 countries.