Method for detecting inconsistent flight data using multi-logical extended functional dependencies

As the number of flights increases worldwide, the issue of flight data quality is of great concern. Data quality plays a key role in all areas, and poor quality flight data can pose significant risks to safety, stability and efficiency. Data consistency is an important indicator of data quality, and rule-based repair is an effective method of data quality repair. The premise of inconsistent data repair is to detect inconsistent data, so it is crucial to detect inconsistent data using algorithms. Although the flight dataset has the characteristics of multiple time dimensions, the traditional integrity constraints and algorithms are not sufficient to fully detect the inconsistent data in the flight data. Therefore, in this paper, we will combine the characteristics of flight data and propose a new integrity constraint: multi-logical extended functional dependency, and an algorithm to achieve inconsistent data detection using this rule. In the detection, we use threshold control to ensure the reasonableness of the detection, and batch processing to improve the running efficiency of the algorithm. The final experimental results show that our algorithm is able to detect more inconsistent data compared to traditional methods.