Exposing Engineering Data As a Strategic Asset with the U.S. Air Force
The Air Force SEEK EAGLE Office (AFSEO) has developed a new data lake architecture and applications that combine historical data, machine learning, and modern big data technology to accelerate complicated engineering analyses. Before a new store (weapon, fuel tank, etc.) is mounted on an aircraft, AFSEO is responsible for setting safe limits for flight for that store and determining the unsafe ways in which different stores can interact. Doing so requires complicated engineering analyses and tests (flight, wind tunnel, etc.) led by Ph.D. level engineers.
During this session, Donna Cotton, CDO the U.S. Air Force SEEK EAGLE Office (AFSEO) will discuss how the Air Force leveraged 50+ years of accumulated data in its data lake to synthesize previously approved analyses rather than building new analysis from scratch.
First, a custom data catalog has been built that tags all files with relevant metadata and then makes them searchable and filterable through a web-based UI. This is enabled by a Dell EMC multiprotocol Isilon storage solution that simultaneously serves files to all operating systems based on users’ roles through SMB/NFS protocols and to the Cloudera based data lake as HDFS. The custom data catalog UI is hosted and served directly by Solr, offering a number of advantages.
Second, a custom application built on Tamr’s machine learning platform searches historical documents for relevant antecedents, and in some cases, can create a “by analogy” certification without human intervention.
Both efforts help engineers certify new stores and capabilities with the agility required to support the modern-day US Air Force.
***As seen at the 2020 MIT Chief Data Officer and Information Quality (CDOIQ) Symposium ***