Data Science & Distributed Computing

Though the mathematics of Data Science strongly resemble classical statistics, the amount of data involved in distributed and cloud computing demands new approaches to the implementation of effective analytical algorithms and efficient information management techniques. Wagner Associates is currently developing distributed analytics for DoD Big Data applications, including Data Fusion and Image Compression (e.g., infrared/hyperspectral imagery). We also have a long history of applying information theoretic approaches to information flow optimization among distributed computing nodes. This page contains some of our notable achievements in Data Science and Distributed Computing.

TruWeather Solutions Prototypes Urban Weather Sensing Infrastructure

DHWA has been mentioned in a recent article regarding our Urban Weather Testbed project.

Information Flow Prioritization Controller (IFPC)

Used within multiple projects for ONR and DARPA, the IFPC provides advanced filtering and compression techniques based on domain awareness and contextual understanding.  Filtering/compression approaches from the IFPC have been implemented within the Open Track Manager (OTM) as Zone Exchange Optimized

Decision Support for Dynamic Target Engagement (DS DTE)

Teamed with Penn State University and its Applied Research Lab, and under a subcontract to Solers, Inc., Daniel H. Wagner Associates developed an advanced information management architecture to provide timely and accurate decision support during dynamic target engagement. This agent-based information

Network Monitoring and Management System (NMMS)

NMMS provides automated monitoring and control of federated sensors using a peer-to-peer (P2P) network

  • Real-time monitoring of network performance, enabling optimization of communications bandwidth for all types of Unmanned Vehicles (UVs) with the Littoral Combat Ship (LCS)
  • Automatic

SOAPi Services™ – Large Scale Integration of Distributed Systems Exposed as SOAP-Based Web Services

This Phase II SBIR project demonstrated efficient and effective agent-based services for military communities of interest (COI) operating on network-centric architectures. Building on the commercial world concept of the enterprise service bus (ESB), the agent-based services (ABS) architecture defines

Situation Awareness Predictive Filter (SAPT).

For US Army CECOM, Wagner conducted a study to reduce communications requirements for SA by using an optimal Kalman Filter to predict target motion and only communicate data when the target position differs sufficiently from the position predicted by the filter.

Distributed Communications Evaluation and Optimization

Wagner Associates has been involved in the analysis and optimization of distributed communications since our early work on the Joint Tactical Information Distribution System (JTIDS) with Johns Hopkins University Applied Physics Laboratory (JHU/APL).