SBIR-STTR Award

Scientific Data Server (CHIMERA)
Profile last edited on: 1/23/2020

Program
SBIR
Agency
AF
Total Award Amount
$899,917
Award Phase
2
Principal Investigator
Christopher J Nolen
Activity Indicator

Company Information

Praeses LLC

330 Marshall Street 8th Floor
Shreveport, LA 71101
   (318) 424-8125
   info@praeses.com
   www.praeses.com
Multiple Locations:   
Congressional District:   04
County:   Caddo Parish

Phase I

Phase I year
2019
Phase I Amount
$149,931
The Air Force Test Center (AFTC) is engaged in the acquisition and analysis of aeronautical telemetry data from flight testsspecifically, HDF5 and IRIG data.Currently, these datatypes are isolated from one another.HDF5 data may be stored in an HDF Server, which provides remote access to large amounts of HDF5 data using a RESTful API.IRIG data, on the other hand, currently lacks a similar system to streamline the ingestion, manipulation, and analysis of the data.This lack of modern server technology for IRIG data combined with the lack of a mechanism to interrelate data results in several challenges in the discovery and analysis of the underlying data:We propose a high performance distributed data server solution named CHIMERA that addresses multiple problems inherent in dealing with large datasets.It addresses data format complexity up front, allowing the system to leverage mainstream proven Big Data technologies for scalability and performance.It then builds upon this by overlaying advanced data analytics techniques to provide users with far more control and insight into the underlying data.CHIMERA segregates data discovery from data querying, data analysis, and data processing.HDF5,IRIG,Telemetry Data,metadata extraction,Graph Database,Big Data,Hadoop,Spark

Phase II

Phase II year
2022 (last award dollars: 2022)
Phase II Amount
$749,986
The Air Force Test Center (AFTC) is engaged in the acquisition and analysis of aeronautical telemetry data from flight tests – specifically, HDF5 and IRIG data. While there is some advanced data management tools for HDF5 data, IRIG data, on the other hand, currently lacks a system to streamline its ingestion, manipulation, and analysis. Additionally, no centralized data management construct exists across the two data types, nor is there a mechanism to interrelate the data. We propose CHIMERA as a high-performance distributed data server solution that addresses multiple problems intrinsic to large datasets. In the Phase I effort, the team performed spike tests to prove out an approach to address data format complexity up front and allow the system to leverage mainstream proven Big Data technologies for scalability and performance. The approach then builds out further by overlaying advanced data analytics techniques to provide users with far more control and insight into the underlying data. CHIMERA segregates data discovery from data querying, data analysis, and data management while providing a unified interface for all three.