Multiscale Fast and Distributed Data and Statistics Summarization

The variety, volume and velocity of data available from the internet, sensors and other sources has grown dramatically providing the opportunity to gain novel insights in many fields.  In order to derive actionable insights from these data sources many challenges must be overcome including the need to summarize or distill large amounts of data that preserve salient features/relationships and methods that leverage scalable processing which minimize data movement. Intelligent Automation Inc, in collaboration with Johns Hopkins University proposes the developed and application of scalable and robust multiresolution summarization algorithms based on Geometric Multi-Resolution Analysis (GMRA) to several relevant problems.  The approach taken has many favorable characteristics including linear scaling with the number of data points, multi-resolution representation of the data, robust to noise, provable error estimates, amenable to fast algorithms, and suitability for visualization and subsequent analysis.  A major thrust of this effort the development of a mature, robust and scalable implementation of the GMRA that is parallelizable on and across compute nodes.  We extend progress made on previous efforts to make the software accessible to a wide audience and relevant to application in several DOD mission areas.