John T. Langton

Applications of multi-objective evolutionary algorithms to air operations mission planning

Air operations mission planning is a complex task, growing ever more complex as the number, variety, and interactivity of air assets increases. Mission planners are responsible for generating as close to optimal taskings of air assets to missions under severe time constraints. This function can be aided by decision-support tools to help ease the search process through automation. This paper presents several applications of multi-objective evolutionary algorithms for discovering suitable plans in the air operations domain, including dynamic targeting for air strike assets, intelligence, surveillance, and reconnaissance (ISR) asset mission planning, and unmanned aerial systems (UAS) planning. Lessons learned from these studies are described and an exploration of potential future directions is discussed.

NeuroVis: combining dimensional stacking and pixelization to visually explore, analyze, and mine multidimensional multivariate data

The combination of pixelization and dimensional stacking uniquely facilitates the visualization and analysis of large, multidimensional databases. Pixelization is the mapping of each data point in some set to a pixel in a 2D image. Dimensional stacking is a layout method where N dimensions are projected onto the axes of an information display. We have combined and expanded upon both methods in an application named NeuroVis that supports interactive, visual data mining. Users can spontaneously perform ad hoc queries, cluster the results through dimension reordering, and execute analyses on selected pixels. While NeuroVis is not intrinsically restricted to any particular database, it is named after its original function: the examination of a vast neuroscience database. Images produced from its approaches have now appeared in the Journal of Neurophysiology and NeuroVis itself is being used for educational purposes in neuroscience classes at Emory University. In this paper we detail the theoretical foundations of NeuroVis, the interaction techniques it supports, an informal evaluation of how it has been used in neuroscience investigations, and a generalization of its utility and limitations in other domains.

Leveraging layout with dimensional stacking and pixelization to facilitate feature discovery and directed queries

Pixelization is the simple yet powerful technique of mapping each element of some data set to a pixel in a 2D image. There are 2 primary characteristics of pixels that can be leveraged to impart information: 1. their color and color-related attributes (hue, saturation, etc.) and 2. their arrangement in the image. We have found that applying a dimensional stacking layout to pixelization uniquely facilitates feature discovery, informs and directs user queries, supports interactive data mining, and provides a means for exploratory analysis. In this paper we describe our approach and how it is being used to analyze multidimensional, multivariate neuroscience data.

Evaluation of current visualization tools for cyber security

Visualization tools for cyber security often overlook related research from the information visualization domain. Cyber security data sets are notoriously large, yet many of the popular analysis tools use 3D techniques and parallel coordinates which have been shown to suffer issues of occlusion when applied to large data sets1,2. While techniques exist to ameliorate these issues they are typically not used. In this paper we evaluate several cyber security visualization tools based on established design principles and human-computer interaction research. We conclude by enumerating challenges, requirements, and recommendations for future work.

Visualization and Interactive Exploration of Large, Multidimensional Data Sets

As biologists work with more and more data, there is an increasing need for effective tools to analyze it. Visualization has long been used to communicate experimental results. It is now being used for exploratory analysis where users rapidly determine significant trends and features by working with visual projections of data. A basic workflow is to a) record experimental results or simulate some biological system, b) form hypotheses, c) verify hypotheses with interactive visualizations and statistical methods, d) revise hypotheses, and e) confirm computational results with experiments in wet-lab.

Visualization for cyber security command and control

To address the unique requirements of cyber Command and Control (C2), new visualization methods are needed to provide situation awareness and decision support within the cyber domain. A key challenge is the complexity of relevant data: it is immense and multidimensional, includes streaming and log data, and comes from multiple, disparate applications and devices. Decision makers must be afforded a view of a) the current state of the cyber battlespace, b) enemy and friendly capabilities and vulnerabilities, c) correlations between cyber events, and d) potential effects of alternative courses of action within cyberspace. In this paper we present requirements and designs for Visualization for Integrated Cyber Command and Control (VIC3).

Combining pixelization and dimensional stacking

The combination of pixelization and dimensional stacking yields a highly informative visualization that uniquely facilitates feature discovery and exploratory analysis of multidimensional, multivariate data. Pixelization is the mapping of each data point in some set to a pixel in an image. Dimensional stacking is a layout method where N dimensions are projected into 2. We have combined both methods to support visual data mining of a vast neuroscience database. Images produced from this approach have now appeared in the Journal of Neurophysiology [1] and are being used for educational purposes in neuroscience classes at Emory University. In this paper we present our combination of dimensional stacking and pixelization, our extensions to these methods, and how our techniques have been used in neuroscience investigations.