Research on Railway Transportation Data Grading Based on Collaborative Context Metamodel

Data grading plays a key role of big data governance and data sharing. Normally data grading method defining data levels depending on scope of influence which caused by leakage and tampering. But Data grading relies on business context and business processes, specially in railway domain. To solve this problem, we propose a method for grading railway data, which allows us to grade context-heavy, multiple types of data. We have sorted out the business processes of railway transportation and organized information about the related business. The information thus extracted from the data enables the construction of context metamodels and the determination of security levels for railway transport data based on inference rules. We selected a typical context of railway transport and used the method to grade the data of the context to verify the effectiveness of our method for grading data with strong Sceneability.