By developing unified data standards and combined with institutional constraints, system control, etc. the integrity, effectiveness, consistency, standardization, openness and sharing management of the data from the big data platform can be realized and to improve the standard of data management in the big

data platform of civil aviation, which mainly includes both basic and industry data standards.

In combining with organizational information form of metadata and to specific describe all forms of digital objects in civil aviation. To define the enterprise’s metadata management in prospect, target, demands, constraints, strategies and so on. To confirm the roadmap of the maturity of metadata management and target accomplishment and to complete the structure of fundamental ontology, domain ontology, task ontology and application ontology and to ensure the security strategies, version control and subscribe metadata to push of the metadata management according to the present and future demands of firm itself.

To provide guidance for business requirement analysis, system function design, research and development of technical framework, service pattern innovation and value realization, which mainly include data storage, data warehouse, metadata, master data, etc.

The key to fulfilling the plan of data governance ideally is how to precisely define and describe business problems. By comprehensively weighing the degree of urgency and the value brought by data governance in civil aviation, and give data governance priority to the top-ranked problems or realms. This will gain full support from both business function and IT department to make sure that the data governance plan will succeed. After the original scope of data governance is settled, the specific work of data governance will be carried out and will be considered to extend to other realms if it worked.

To meet the demands and targets of master data management, we do the research by spotting MDM objects [7] , confirming master data owners, establishing MDM organizations, regulating MDM process, unifying master data standards, analyzing and building the position and integrated frame requirements of MDM system. In addition, a MDM system in civil aviation will be researched and planned.

On the platform of big data in civil aviation, data quality provides it with clean and explicit-structured data, which is vital premise to develop data products and provide data services and bring value to big data on the platform. And also the key factor to the data assets management of civil aviation.

It mainly focuses on governing the data classification quality measures, data quality check definitions, data quality check implementation, data quality check analysis, data quality issues management, data quality management knowledge and so on.

According to the characteristics of data, using distributed information sharing based on agent structure and solving the problem of information synergy technology by XML-agent technique [8] . The whole system can be divided into the master data base, data engine, directory information center, information node, agent access, etc.

Data security policy and security policy measures are established to provide effective approval, authorization, access and audit [9] . Achieving detailed implementation plan of sensitive data access and realizing privacy protection technology and management action of customer privacy protection. With enhancing the technical safeguard of data security.

Through the exploration, cleaning, integration, monitoring and information mining of civil aviation business master data, the master data quality will be improved. With the combination of data management and civil aviation enterprise organization, the data integration and mining application will be provided a strong guarantee. Specific governance framework is shown in Figure 2.

1) Metadata integration architecture based on CWM model driven

It mainly focuses on metadata integration architecture based on common warehouse met a model. The architecture uses the MOF model based on formal and independent of platform specific implementation, uses UML formal language to describe the information structure, constructs exchange metadata based on the CWM model which is public and outside [10] , achieves mutual understanding between models, solves the problems between the metadata translation model, greatly reduces the metadata bridge, so as to improve the enterprise data integration ROI.

2) Dynamic metadata cluster management based on consistent Hash structure

It mainly focuses on the dynamic metadata cluster management scheme based on the consistency of Hash, which can realize the efficient management and flexible expansion of the cluster metadata in the mass data storage system, and reduce the data migration cost. Through adding virtual metadata server structure to the consistency in Hash structure and extending the single and unbalanced mapping which is from metadata to the metadata server to the balanced

mapping which is from metadata to virtual metadata server and metadata server, it can improve the whole service metadata server cluster load balancing work.

3) Establishing formal reasoning framework of metadata

In the process of establishing metadata based on the common warehouse model (CWM) [11] , different experiences of participating in the establishment of metadata and different perspectives of the data description inevitably leads to the problems of conflict and redundancy of metadata. However, the graphical features of CWM make it lack of precise semantics. In the metadata integration architecture in the process of research, this project focuses on how to reasoning on CWM to automatically detect these problems, and studies the method of making use of the CWM model and metadata and reasoning, and we can enables it to support the ability of metadata version, so as to reasoning inconsistent problems caused by evolution, then inference engine is used to detect the inconsistent information.

4) Quality management of big data of civil aviation based on PDCA methodology

Through the cycle process of the planning, implementation, inspection, analysis and adjustment, this study focuses on quality management of big data of civil aviation based on PDCA method.

5) Maturity evaluation of governance structure of big data of civil aviation based on CMM

Based on the analysis of the classification method of the capability maturity model (CMM) [12] , this study evaluates the maturity of governance of big data of civil aviation data, and puts forward the optimization strategy of the governance process according to the evaluation results.