Semantic, Real-Time, Streaming Data Compression for MDA Modeling and Simulation Systems

We propose to explore a number of targeted approaches for data reduction that go beyond what existing algorithms can do.These include semantic, predictive, preprocessing, protocol, and rule-based compression techniques that will be tuned for M&S systems.We will construct representative datasets, and conduct performance evaluations against the leading general data compression techniques in use today.Moreover, because there is no one-size-fits-all data compression algorithm for all types of data, we propose to provide an openly pluggable framework that enables different (configurable) algorithms to be applied to different types of dataproviding content and context-driven compression.Our system will adaptively apply compression algorithms in real-time, supporting both heterogeneous and evolving streams of data.This will serve as a highly configurable and pluggable foundation for all M&S compression requirements.Approved for Public Release | 17-MDA-9395(24 Oct 17)