Core Area: Visual Design
We develop new ways to tell stories with data through visual representations. We create aesthetic depictions for complex patterns and relationships that summarize visually the output of our information signatures.
Research Topics and Products
Arc Weld
ArcWeld is a new analytic visualization for displaying and understanding large diverse sets of multimedia data through abstracting, segmenting and displaying relationships through interchangeable attributes. The visualization is further enhanced through novel interaction techniques that allow the user to pivot, browse, and search and drill in deeper for further insight.
Arcweld gives an analyst a quick summary of a diverse set data and allows them to discover further insight by digging deeper into its content through various attributes by arranging, visualizing relationships and delving deeper into the data.
Arc Weld is a visualization in the Canopy suite of tools.
SRS Lessons Learned Explorer
At PNNL, lessons learned in critical areas such as safety, management, and security are captured in articles that are shared across the lab on an internal website that once had limited search capabilities. To improve access to this information, our Scalable Reasoning System (SRS) team used its web-based analytics framework to create the Lessons Learned Explorer (LLEx). LLEx implements several SRS widgets that vastly improved the searchability and usability of the Laboratory's lessons learned. For instance, the word cluster widget analyzes unstructured text, partitioning the document collection into clusters using differentiating words detected within it. A faceted browse widget lets users explore different dimensions of structured, categorical data to find relevant articles based on known properties of the data. The treemap widget visually displays a subset of the categories used in the faceted browse widget, allowing users to quickly see the importance of certain articles among all categories. The Story Flow widget identifies prominent themes in data and depicts their change over time.
Story Flow
The Story Flow visualization shows for a set of time intervals, the themes computed for those intervals, with size indicating the number of documents in that theme, which may link themes over time into stories. The visualization places days across the horizontal axis and orders daily themes along the vertical axis. Themes are consistently ordered for each interval by their number of assigned documents so that the theme order for each day is unaffected by future days. This preserves the organization of themes in the story flow visualization across days and supports information consumers’ extended interaction over days and weeks. An individual or team would therefore be able to print out each day’s story flow column with document titles and lines, and post that next to the previous day’s columns.
The Story Flow visualization has been implemented in several applications, namely IN-SPIRE™ and SRS.
Related Papers
Show all abstractsEvents and Trends in Text Streams
Engel DW, PD Whitney, and NO Cramer. 2010. Events and Trends in Text Streams. Chapter 9 in Text Mining: Application and Theory, vol. 1, ed. MWBerry, J Kogan, pp. 3-20. John Wiley & Sons, Chichester, United Kingdom.
Abstract
Text streams--collections of documents or messages that are generated and observed over time--are ubiquitous. Our research and development are targeted at developing algorithms to find and characterize changes in topic within text streams. To date, this research has demonstrated the ability to detect and describe 1) short duration, atypical events and 2) the emergence of longer-term shifts in topical content. This technology has been applied to predefined temporally ordered document collections but is also suitable for application to near-real-time textual data streams.
Describing Story Evolution from Dynamic Information Streams
Rose SJ, RS Butner, WE Cowley, ML Gregory, and J Walker. 2009. Describing Story Evolution from Dynamic Information Streams. In IEEE Symposium on Visual Analytics Science and Technology (IEEE VAST) VAST 2009, Oct. 12-13, 2009, Atlantic City, NJ, pp. 99-106. IEEE , Piscataway, NJ.
Abstract
Sources of streaming information, such as news syndicates, publish information continuously. Information portals and news aggregators list the latest information from around the world enabling information consumers to easily identify events in the past 24 hours. The volume and velocity of these streams causes information from prior days' to quickly vanish despite its utility in providing an informative context for interpreting new information. Few capabilities exist to support an individual attempting to identify or understand trends and changes from streaming information over time. The burden of retaining prior information and integrating with the new is left to the skills, determination, and discipline of each individual. In this paper we present a visual analytics system for linking essential content from information streams over time into dynamic stories that develop and change over multiple days. We describe particular challenges to the analysis of streaming information and explore visual representations for showing story change and evolution over time.
The Scalable Reasoning System: Lightweight Visualization for Distributed Analytics
Pike W, J Bruce, B Baddeley, D Best, L Franklin, R May, D Rice, R Riensche, and K Younkin. 2009 The Scalable Reasoning System: Lightweight Visualization for Distributed Analytics. Information Visualization. 8(1): 71-84.
Abstract
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 'lightweight' 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.
A Novel Visualization Technique for Electric Power Grid Analytics
Wong PC, K Schneider, P Mackey, H Foote, G Chin, R Guttromson, and J. Thomas. 2009 A Novel Visualization Technique for Electric Power Grid Analytics. Visualization and Computer Graphics, IEEE Transactions on 15(3):410-423
Abstract
The application of information visualization holds tremendous promise for the electric power industry, but its potential has so far not been sufficiently exploited by the visualization community. Prior work on visualizing electric power systems has been limited to depicting raw or processed information on top of a geographic layout. Little effort has been devoted to visualizing the physics of the power grids, which ultimately determines the condition and stability of the electricity infrastructure. Based on this assessment, we developed a novel visualization system prototype, GreenGrid, to explore the planning and monitoring of the North American Electricity Infrastructure. The paper discusses the rationale underlying the GreenGrid design, describes its implementation and performance details, and assesses its strengths and weaknesses against the current geographic-based power grid visualization. We also present a case study using GreenGrid to analyze the information collected moments before the last major electric blackout in the Western United States and Canada, and a usability study to evaluate the practical significance of our design in simulated real-life situations. Our result indicates that many of the disturbance characteristics can be readily identified with the proper form of visualization.
Analytics for Massive Heat Maps
Love D, S Bohn, D Payne, and G Nakamura, Grant. 2009. Analytics for Massive Heat Maps. SPIE Visualization and Data Analysis conference, San Jose, 19 January 2009.
Abstract
High throughput instrumentation for genomics is producing data orders of magnitude greater than even a decade before. Biologists often visualize the data of these experiments through the use of heat maps. For large datasets, heat map visualizations do not scale. These visualizations are only capable of displaying a portion of the data, making it difficult for scientists to find and detect patterns that span more than a subsection of the data. We present a novel method that provides an interactive visual display for massive heat maps [O(108)]. Our process shows how a massive heat map can be decomposed into multiple levels of abstraction to represent the underlying macrostructures. We aggregate these abstractions into a framework that can allow near real-time navigation of the space. To further assist pattern discovery, we ground our system on the principle of focus+context. Our framework also addresses the issue of balancing the memory and display resolution and heat map size. We will show that this technique for biologists provides a powerful new visual metaphor for analyzing massive datasets.