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.
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
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
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
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