OpenBARR combines advanced machine learning, big-data analytics, and the vast computing power of the cloud to isolate specific aircraft movements out of multiple public data sources. This allows us to find aircraft that are filtered from normally referenced data sources like ASDI by the BARR, or using alternate or false call-signs or tail numbers.
Conceived of in 2012 by Dustin E. Hoffman, the initial techniques were implemented and tested and found to be useful and reasonably accurate given sufficient training. Limitations in available data and software available at the time, along with a long software training cycle requirement rendered our initial efforts not commercially viable.
Information about early work on the project was presented at DEFCON 20 in Las Vegas, NV.
Since 2012, the initial concept has been repeatedly refined and, along with integrating multi-dimensional analytics and entirely new data sources, has reached an effectiveness level that makes it commercially viable.