CROSS-PLATFORM AVIATION ANALYTICS USING BIG-DATA METHODS

This paper identifies key aviation data sets for operational analytics, presents a methodology for application of big-data analysis methods to operational problems, and offers examples of analytical solutions using an integrated aviation data warehouse. Big-data analysis methods have revolutionized how both government and commercial researchers can analyze massive aviation databases that were previously too cumbersome, inconsistent or irregular to drive high-quality output. Traditional data-mining methods are effective on uniform data sets such as flight tracking data or weather. Integrating heterogeneous data sets introduces complexity in data standardization, normalization, and scalability. The variability of underlying data warehouse can be leveraged using virtualized cloud infrastructure for scalability to identify trends and create actionable information. The applications for big-data analysis in airspace system performance and safety optimization have high potential because of the availability and diversity of airspace related data. Analytical applications to quantitatively review airspace performance, operational efficiency and aviation safety require a broad data set. Individual information sets such as radar tracking data or weather reports provide slices of relevant data, but do not provide the required context, perspective and detail on their own to create actionable knowledge. These data sets are published by diverse sources and do not have the standardization, uniformity or defect controls required for simple integration and analysis. At a minimum, aviation big-data research requires the fusion of airline, aircraft, flight, radar, crew, and weather data in a uniform taxonomy, organized so that queries can be automated by flight, by fleet, or across the airspace system.