Enhancing Intelligent Aviation Operations Using Process Mining Techniques

Today, the aviation industry plays a significant role in relations between different countries of the world, demonstrating the economic and military power and extending essentials of a country. Improving the performance of the aviation industry by analyzing big data available using intelligent methods, increases the efficiency of this system. According to the previous studies, despite the importance of this industry and large amount of data, there has not been adequate attention using data-based methods in past studies in order to discover knowledge in this industry. The process mining is one of these intelligent methods that allows automatically obtaining information from event log data and analyzing system processes. This research focuses on the implementation of the process discovery method with control-flow and time pesrpective using Prom software to investigate intelligent prediction of occurrence time of key events in the field of aviation system. In this research, after extracting the initial flight data from Zagros Airlines and converting them to the event log, the flights event log is given to the Prom software and are extracted the transition system model and time prediction of key flight events. Some innovations of this research include using time prediction method of the process mining approach on the actual data, time prediction and analyse the time behavior of the key events of future flights dynamically based on the past behavior of each flight. By using the results of this study, waiting time which prevents from passengers’ congestion is reduced and because of high satisfaction of passengers with up-to-date flights information, demand for aviation system is increased. It also helps to create intelligent scheduling in the aviation system and improves the scheduling of airport facilities.