Lyndsey Franklin

The Scalable Reasoning System: Lightweight visualization for distributed analytics

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 ldquolightweightrdquo 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.

The scalable reasoning system: lightweight visualization for distributed analytics

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.

Automating Frame Analysis

Frame Analysis has come to play an increasingly stronger role in the study of social movements in Sociology and Political Science. While significant steps have been made in providing a theory of frames and framing, a systematic characterization of the frame concept is still largely lacking and there are no recognized criteria and methods that can be used to identify and marshal frame evidence reliably and in a time and cost effective manner. Consequently, current Frame Analysis work is still too reliant on manual annotation and subjective interpretation. The goal of this paper is to present an approach to the representation, acquisition and analysis of frame evidence which leverages Content Analysis, Information Extraction and Semantic Search methods to automate frame annotation and provide a systematic treatment of Frame Analysis.

Cognitive Foundations for Visual Analytics

In this report, we provide an overview of scientific/technical literature on information visualization and VA. Topics discussed include an update and overview of the extensive literature search conducted for this study, the nature and purpose of the field, major research thrusts, and scientific foundations. We review methodologies for evaluating and measuring the impact of VA technologies as well as taxonomies that have been proposed for various purposes to support the VA community. A cognitive science perspective underlies each of these discussions.

Toward a visualization-supported workflow for cyber alert management using threat models and human-centered design

Cyber network analysts follow complex processes in their investigations of potential threats to their network. Much research is dedicated to providing automated decision support in the effort to make their tasks more efficient, accurate, and timely. Support tools come in a variety of implementations from machine learning algorithms that monitor streams of data to visual analytic environments for exploring rich and noisy data sets. Cyber analysts, however, need tools which help them merge the data they already have and help them establish appropriate baselines against which to compare anomalies. Furthermore, existing threat models that cyber analysts regularly use to structure their investigation are not often leveraged in support tools. We report on our work with cyber analysts to understand the analytic process and how one such model, the MITRE ATT&CK Matrix [42], is used to structure their analytic thinking. We present our efforts to map specific data needed by analysts into this threat model to inform our visualization designs. We leverage this expert knowledge elicitation to identify a capability gaps that might be filled with visual analytic tools. We propose a prototype visual analytic-supported alert management workflow to aid cyber analysts working with threat models.

Rethinking Visual Analytics for Streaming Data Applications

Visual analytics is entering a period of renewed growth due to a shift in focus from static to streaming data applications. In this article, the authors illustrate several challenges arising from this pivot and suggest potential avenues for future exploration.

Cybersecurity Awareness in the Power Grid

We report on a series of interviews and observations conducted with control room dispatchers in a bulk electrical system. These dispatchers must react quickly to incidents as they happen in order to ensure the reliability and safe operation of the power grid. They do not have the time to evaluate incidents for signs of cyber-attack as part of their initial response. Cyber-attack detection involves multiple personnel from a variety of roles at both local and regional levels. Smart grid technology will improve detection and defense capabilities of the future grid, however, the current infrastructure remains a mixture of old and new equipment which will continue to operate for some time. Thus, research still needs to focus on strategies for the detection of malicious activity on current infrastructure as well as protection and remediation.

Visual analytics for law enforcement: deploying a service-oriented analytic framework for web-based visualization

This paper presents key components of the Law Enforcement Information Framework (LEIF), an information system that provides communications, situational awareness, and visual analytics tools in a service-oriented architecture supporting web-based desktop and handheld device users. LEIF simplifies interfaces and visualizations of wellestablished visual analytic techniques to improve usability. Advanced analytics capability is maintained by enhancing the underlying processing to support the new interface. LEIF development is driven by real-world user feedback gathered through deployments at three operational law enforcement organizations in the U.S. The system incorporates a robust information ingest pipeline supporting a wide variety of information formats. LEIF also insulates interface and analytical components from information sources making it easier to adapt the framework for many different data repositories.

Evaluation of Visualization Heuristics.

Multiple sets of heuristic have been developed and studied in the Human Computer Interaction (HCI) domain as a method for fast, lightweight evaluations for usability problems. However, none of the heuristics have been adopted by the information visualization or the visual analytics communities. Our literature review looked at heuristic sets developed by Nielsen and Molich [7] and Forsell and Johansson [1] to understand how these heuristics were developed and their intended applications. We also reviewed heuristic studies conducted by Hearst and colleagues [2] and Vaataja and colleagues [10] to determine how individuals apply heuristics to evaluating visualization systems. While each study noted potential issues with the heuristic descriptions and the evaluator’s familiarity with the heuristics, no direct connections were made. Our research looks to understand how individuals with domain expertise in information visualization and visual analytics could use heuristics to discover usability problems and evaluate visualizations. By empirically evaluating visualization heuristics, we can identify the key ways that these heuristics can be used to inform the visual analytics design process. Further, they may help to identify usability problems that are and are not task specific. We hope to use this process to also identify missing heuristics that may apply to designs for different analytic purposes.

Employing a User-Centered Design Process for Cybersecurity Awareness in the Power Grid

In this paper, we discuss the process we are using in the design and implementation of a tool to improve the situation awareness of cyberattacks in the power grid. We provide details of the steps we have taken to date and describe the steps that still need to be accomplished. The focus of this work is to provide situation awareness of the power grid to staff from different, non-overlapping roles in an electrical transmission organization in order to facilitate an understanding of a possible occurrence of a cyberattack. Our approach follows a user-centered design process and includes determining the types of information to display, the format of the displays, and the personnel to whom the display should be shown. Additionally, there is the issue of how much help the tool can provide in the way of assessing the probability of a cyberattack given the current status of various portions of the power grid. Regardless, the ability to provide a common operating picture should enable the various groups to collaborate on a response.