Provide a Big Data technology-driven architecture with safety prognostics capability in supporting RSSA that can address safety risk/hazard identification techniques on large quantities of historical NAS data and streaming live aviation data. Assist ATM researchers directly by enhancing the capabilities of the ATM Data Warehouse with these techniques. Allow ongoing data mining efforts to utilize Big Data technology to enhance the performance of these safety algorithms, dramatically allowing for faster discovery of more safety or performance anomalies and eventually predicting safety risk and precursors in near real time. Enhance the capabilities of SMART-NAS for researchers to quickly examine the system-wide safety implications of new concepts and technologies, and address the design and operational mitigations of safety risks.
Enable key FAA operational facility safety personnel to have performance dashboards driven by Big Data-based, near real-time analytics to alert them when the NAS or regional areas are experiencing or could experience safety risk. Allow airlines to turn vast amounts of FOQA data and other information gathered from operations into "actionable" information by improving turnaround time for analysis and expanding the range of questions that can be asked of the data sets that they do maintain. Enable airline safety personnel to monitor and predict their fleet and pilot safety performance to better predict where accidents might happen using the large amount of FOQA and/or other airlines' data that the airlines have been collecting. Enable international Air Navigation Service Provider safety personnel to monitor and predict system threats.
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