Research On Model Of Engineering Data Warehouse For Scientific Experimentation

It is a hotspot to study the integration of engineering data in Information Supply Chain (ISC), which is an engineering data flow in distributed and heterogeneous engineering applications of Scientific Experimentation. Meta data integration is a key prerequisite to ensure the effectiveness of engineering data flow in ISC and it appears much complicated due to the features of engineering data, such as separate organization, complex and variable schemas, lack of uniform structure, etc. Based on the development of engineering applications of Scientific Experimentation and Data Warehouse technology, this thesis primarily focuses on the model-driven method about meta data integration. This thesis firstly analyzes the characteristics of engineering data, and summarizes the concept ontology of engineering applications. Then a comprehensive strategy of meta data management and a complete model-based meta data integration solution are proposed, and a four-layer meta modeling framework based on the semantics agreement of each ontology concept which used to model Engineering Data Warehouse (EDW) is defined. Secondly, a model-driven architecture of meta data integration is introduced. A shared and platform-independent metamodel is defined according to Common Warehouse Metamodel (CWM) which is adopted standard by the Object Management Group (OMG). As a formal language capable of representing meta data, the metamodel enables different domain models to represent EDW in an interoperable manner. And then a domain-specific metamodel is designed aims at On-Line Analytical Processing (OLAP) in EDW. Finally, the specific features of OLAP scenario related with EDW are analyzed. The implementation of logical modeling and physical deployment modeling with OLAP metamodel and Mapping metamodel is discussed in detail from the perspective of an end user of the system.