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Marshall MRMS Mosaic Python Toolkit (MMM-Py)

Marshall Space Flight Center, Alabama This Python script will allow the user to read, analyze, and display National Oceanic and Atmospheric Administration (NOAA) Multi-Radar/Multi-Sensor(MRMS) mosaic tile files containing mosaic radar reflectivities on a national 3D grid. Simple diagnostics and plotting, as well as computation of composite reflectivity, are available.

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Data Encoding and Parallelization Porting Techniques to Transform Binary Data Formats to Hadoop/MapReduce

Goddard Space Flight Center, Greenbelt, Maryland The objective of this invention is to transform applications that process big data in arbitrary formats using the MapReduce parallel programming model and executed on the open-source Hadoop platform. Many legacy data-intensive applications do not scale out of a single server. Some applications can load-balance to multicore, but scalability is limited by intensive IO. Hadoop/MapReduce is a massively scalable programming paradigm modeled after Google’s implementation. There are many use cases to show Hadoop/MapReduce can scale linearly to hundreds or even thousands of servers for big data analysis. The problem is how to transparently transform a legacy serial data processing code to take full advantage of Hadoop/MapReduce parallel processing framework.

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Framework Software Library Version 1.0

Goddard Space Flight Center, Greenbelt, Maryland Within the space community, there is a need to exchange a wide variety of data between partner organizations. The Framework software library can be used to exchange any type of data between partners. It provides these core capabilities:

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Crisis Mapping Toolkit (CMT) V1

Ames Research Center, Moffett Field, California The increasing availability of accessible geospatial data with a turnaround time of days or hours provides unique opportunities for responders to better plan responses to crises, and to inform victims, friends, and relatives of local crisis conditions. However, this raw data is not readily interpretable by the general public.

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Real-Bogus Machine Learning Systems at the Intermediate Palomar Transient Factory

NASA’s Jet Propulsion Laboratory, Pasadena, California The intermediate Palomar Transient Factory (iPTF) is a wide-field sky survey of the optical transient sky (e.g., supernovae, variable stars) that uses image subtraction for the discovery of astronomical transients. Astronomical transients, such as supernovae, are only observable on a timescale of weeks to months. In order to thoroughly study supernova physics, it is important to detect the supernova as early as possible to trigger follow-up assets that can begin observing the event in multiple wavelengths prior to its decline.

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Scalable Gaussian Process Regression

For multimodal data, this method gives higher prediction accuracy than a single approximate model. Ames Research Center, Moffett Field, California Block GP is a Gaussian Process regression framework for multimodal data that can be an order of magnitude more scalable than existing state-of-the-art nonlinear regression algorithms. The framework builds local Gaussian Processes on semantically meaningful partitions of the data and provides higher prediction accuracy than a single global model with very high confidence. The method relies on approximating the covariance matrix of the entire input space by smaller covariance matrices that can be modeled independently, and can therefore be parallelized for faster execution.

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MBSE-Driven Systems Engineering Visualization Suite

NASA’s Jet Propulsion Laboratory, Pasadena, California There is a need to define the information architecture, ontologies, and patterns that drive the construction and architecture of Model-Based Systems Engineering (MBSE) models, but less clarity is given to the logical follow-on of that effort: how to practically leverage the resulting semantic richness of a wellformed populated model.

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