Stuart J. Rose

Describing story evolution from dynamic information streams

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 present a fundamental visual representation for showing story change and evolution over time.

Walking the path: a new journey to explore and discover through visual analytics

Under the leadership of the US Department of Homeland Security (DHS), researchers at the Pacific Northwest National Laboratory (PNNL) established a research center focusing on the discipline of visual analytics in 2004. A year later, the center led a multidisciplinary panel representing academia, industry, and government to formally define directions and priorities for future research and development (R&D) for visual analytics tools. The R&D agenda, Illuminating the Path, defines the term visual analytics as ‘the science of analytical reasoning facilitated by interactive visual interfaces’. This article describes our progress to date in walking that path. We briefly describe the background of the subject, present major professional activities and accomplishments of its community, and highlight some of the ongoing R&D efforts being carried out by researchers at PNNL to fulfill the requirements and missions of a new discipline that promises to change the way we deal with todays information.

Understanding the dynamics of collaborative multi-party discourse

In this paper, we discuss the efforts underway at the Pacific Northwest National Laboratory in understanding the dynamics of multi-party discourse across a number of communication modalities, such as email, instant messaging traffic and meeting data. Two prototype systems are discussed. The Conversation Analysis Tool (ChAT) is an experimental test-bed for the development of computational linguistic components and enables users to easily identify topics or persons of interest within multi-party conversations, including who talked to whom, when, the entities that were discussed, etc. The Retrospective Analysis of Communication Events (RACE) prototype, leveraging many of the ChAT components, is an application built specifically for knowledge workers and focuses on merging different types of communication data so that the underlying message can be discovered in an efficient, timely fashion.

SociAL Sensor Analytics: Measuring phenomenology at scale

The objective of this paper is to present a system for interrogating immense social media streams through analytical methodologies that characterize topics and events critical to tactical and strategic planning. First, we propose a conceptual framework for interpreting social media as a sensor network. Time-series models and topic clustering algorithms are used to implement this concept into a functioning analytical system. Next, we address two scientific challenges: 1) to understand, quantify, and baseline phenomenology of social media at scale, and 2) to develop analytical methodologies to detect and investigate events of interest. This paper then documents computational methods and reports experimental findings that address these challenges. Ultimately, the ability to process billions of social media posts per week over a period of years enables the identification of patterns and predictors of tactical and strategic concerns at an unprecedented rate through SociAL Sensor Analytics (SALSA).

DriftWeed: a visual metaphor for interactive analysis of multivariate data

We present a visualization technique that allows a user to identify and detect patterns and structures within a multivariate data set. Our research builds on previous efforts to represent multivariate data in a 2D information display through the use of icon plots. Although the icon plot work done by Pickett and Brinstein is similar to our approach, we improve on their efforts in several ways. Our technique allows analysis of a time series without using animation; promotes visual differentiation of information clusters based on measures of variance; and facilitates exploration through direct manipulation of geometry based on scales of variance. Our goal is to provide a visualization that implicitly conveys the degree to which an elements ordered collection of attributes varies from the prevailing pattern of attributes for other elements in the collection. We apply this technique to multivariate abstract data nd use it to locate exceptional elements in a data set and divisions among clusters.

ChAT: A Time-Linked System for Conversational Analysis

We present a system for analyzing conversational data. The system includes state-of-the-art natural language processing components that have been modified to accommodate the unique nature of conversational data. In addition, we leverage the added richness of conversational data by analyzing various aspects of the participants and their relationships to each other. Our tool provides users with the ability to easily identify topics or persons of interest, including who talked to whom, when, entities that were discussed, etc. Using this tool, one can also isolate more complex networks of information: individuals who may have discussed the same topics but never talked to each other. The tool includes a UI that plots information over time, and a semantic graph that highlights relationships of interest.

Morphing Terminology Study

This study investigates methods of automatically identifying and characterizing significant transitions in term usage over time. Within scientific literature, the occurrence of terms reflects the use of technologies and techniques as well as the study of specific species and materials. Transitions in terminology usage may be a result of vocabulary standardization or specialization in which terms are replaced with their shorter form. They may also be a result of new applications, combinations, alternatives, or interests that result in the appearance of new or existing terminology in unexpected contexts.

Visually Exploring Worldwide Incidents Tracking System Data

This paper presents and explores the application of a visualization and analysis tool - Juxter - as an interface for exploration of incidents described within the Worldwide Incidents Tracking System and describes several refinements that improve user interactions and aid identification of patterns and trends.