Comprehensive Space Object Catalog built on Objectivity/DB will reduce risk of future satellite collisions
The Space Situational Awareness Foundational Enterprise (SSAFE) program will be used by the U.S. Air Force to predict and prevent collisions
For Immediate Release:
Sunnyvale, CA -- February 23, 2009 --
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Using the Objectivity/DB-powered system, U.S. Air Force personnel will be able to track space objects in real-time, so that decisions about spacecraft placement and collision avoidance can be made in seconds, rather than hours or days. Objectivity/DB will also allow the system to scale nearly infinitely as the catalog grows larger.
Tracking space debris for collision avoidance and human flight safety has been a priority for government space agencies since the launch of Sputnik 1 in 1957. Yet the most recent satellite collision has revealed a dangerous void in tracking capabilities, proving that without a comprehensive system, the number of space objects in orbit will only continue to increase.
"By tracking these objects in space, the U.S. Air Force will be able to predict threats and use that information to avoid catastrophic collisions in the future," said Jay Jarrell, Objectivity's president and CEO. "Our technology is being used to reduce the risk of space object collisions, diminish the creation of space debris, and most importantly, ensure human flight safety."
Objectivity was selected as the database of choice to demonstrate the advantages of a distributed database that allows concurrent data access for a higher aggregate throughput and scalable features to accommodate mission growth. Objectivity/DB enables predictive and preventive analytics that can be used to avoid future collisions.
Objectivity/DB also satisfied a crucial requirement for the Air Force's implementation of SSAFE - it allows Air Force personnel to run existing FORTRAN algorithms without modification, therefore preserving vital legacy data.A note on Objectivity/DB
Objectivity/DB is a fully scalable, distributed processing architecture that manages localized, centralized or distributed databases by synthesizing large, complicated streams of data into a single logical view.
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