What’s New:

August 2023

RNET has recently been awarded a Department of Energy Phase II SBIR to develop a Machine Learning Based Data Compression (MLDC) algorithm for numeric simulation data.

July 2023

RNET has recently been awarded a NASA Phase I SBIR to develop a Rapid Data Analytics Platform using machine learning uses a novel dimensional reduction algorithm.

June 2023

RNET has recently been awarded an NIH Phase II SBIR to design and develop Machine Learning tools to help Pathologists overcome the limitations of current computing hardware to design more accurate deep learning models for use in clinical diagnostics. These models will be able to analyze very large digitized images of glass slides (i.e., Whole Slide Images) to aid pathologists in tasks like cancer detection. The ability to analyze these images in their entirety instead of in small parts will improve the diagnostic accuracy of models and will accelerate algorithm development efforts.

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MapReduce is a very popular data analytic framework that is widely used in both industry and scientific research. Despite the popularity of MapReduce, there are several obstacles to applying it for developing some commercial and scientific data analysis applications. iNFORMER is a “A MapReduce-like Data-Intensive Processing Framework for Native Data Storage and Formats” that addresses many of the drawbacks of using MapReduce on scientific data.

The framework allows MapReduce-like applications to be executed over data stored in a native data format, without first loading the data into the framework. This addresses a major limitation of existing MapReduce-like implementations that require the data to be loaded into specialized file systems, e.g., the Hadoop Distributed File System (HDFS). The overheads and additional data management processes required for this translation can prevent MapReduce from being used in many commercial and scientific environments.