Traditionally, big data generated by the Army Test and Evaluation (T&E) community must be collected, processed, and stored before data analysis can occur. These phases of the big data life cycle cause a delay between when data were generated and when actionable insights are available to users. As a potential solution, we present a machine learning based approach for immediate analysis of big data collected during testing events. We utilized historical instrumentation datasets to train an anomaly detection model. This model was then used to label anomalies in the historical data records. Once the data was labelled, we trained a random forest model to classify based on the anomaly labels. This was then used to find feature importance scores in the datasets. We were able to successfully detect anomalies and determined which features were optimal for visualizing anomalous data points.

We present multiple modes of visualizing a largescale Monte Carlo simulation. Due to disparity of the data, there is no one best visualization technique to understand the results of the simulation. By connecting the visualization methods directly to the simulation, we are able to visually analyze the simulation as it occurs in real time. This includes viewing the parameters of the model, the relationships among variables, and the 3D environment in which the simulation takes place. Furthermore, these environments may be coordinated for one seamless visual analytic experience.

We present a visualization architecture to support 2D and 3D visual analytics applications. The architecture is designed to be data-flow-oriented and reconfigurable such that several diverse visualization components can operate as one integrated system. Our prototype application allows users to visually analyze the results of a complex 3D network simulation data both on large high-resolution display and HTC Vive Head Mounted Display. The network simulation outputs variables describing various characteristics of network connectivity between the moving nodes on the ground and in the air interacting in a dynamically changing 3D environment. Our system uses 2D charting tools to visualize the statistical relationships between simulation variables. We developed a Unity application to animate the network simulation in a virtual environment showing the timevarying results in a 3D environment. The Unity application runs on a complete-immersive Head Mounted Display device. The 2D visualization framework running on our Large High-Resolution Display system supports multiple coordinated views across all the different 2D visualization components including a 2D map. Preliminary results show our data-centric design provides a usercentric visualization tool that can greatly enhance the analytical process and speed up the derivation of insights from data.

Tactical network environments are complex, resource constrained, highly mobile in nature and performance of the communication links between the nodes is significantly affected by the path loss caused by the buildings. In this paper, we have used the ns-3 discrete event simulator to simulate an urban tactical network environment using the Rosslyn, VA city model. The simulated model was visualized using unity/worldwind. We have calculated path loss data using VPL and injected the path loss data into link selection to visually study the effects of path loss on the link formation in urban environments.

We present a ParaViewWeb based visual analytics application running on large high-resolution display supporting standard mouse and keyboard interaction. The application relies on SAGE2 for user interaction and multi-display visualization. We also employ a scalable middleware system called "Cloudberry" that allows users to interactively query and analyze large amounts of temporal and spatial data stored on a back end Apache AsterixDB store to enable big data analytics and interactive visualization. Our Visual Analyzing Billion Tweets application shows interactive query and visualization of result from over a billion twitter feeds streamed in real-time to the back end Apache AsterixDB. In our setup, we ran the visual analytics application on a large high-resolution display with a 24-tiled display in a 6 x 4 configuration. We also run a comparative study of the application running on a single 24 inch display and the 24-tiled display with some very interesting findings supporting the benefit of using large high-resolution display for visual analytics.