USING APACHE SPARK TO SPEED ANALYSIS OF ADS-B AIRCRAFT-TRACKING DATA TECHNIQUES
Zhou, Jim Z.
Rowe, Neil C.
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The U.S. Navy is exploring the feasibility of using a big-data platform and machine-learning algorithms to analyze combat-identification data. Combat identification involves a large number of remote sensors that report back data for aggregation and analysis. In this thesis, we used a sample of ADS-B aircraft-tracking data to test big-data methods for machine-learning methods developed previously. We showed large speed improvements in the analysis setup over the previous single-processor methods, and a lesser speed improvement for machine-learning based anomaly analysis.
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