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Mastering Data Warehouse DesignRelational and Dimensional Techniques phần 10

Mỗi thực thể là đối tượng được chỉ định đến khu vực và diện tích phân chủ đề rõ ràng. Nếu một người quản lý dữ liệu đặc biệt hoặc dữ liệu bạn Trách nhiệm xây dựng mô hình một vấn đề cụ thể, Sau đó, tất cả các dữ liệu cho người nào đó chịu trách nhiệm là ở một nơi. Thông tin có thể được lấy ra dễ dàng cho các môn học cụ thể. | Comparison of Data Warehouse Methodologies 395 For the MD approach the multidimensional or star schema data model is easy to understand by the business community. The data model is generally less complex and resembles the way many business community members think about their data that is they think in terms of multiple dimensions for example Give me all the sales revenues for each store in each city and state by market segment over the last two months. Thus it is also easier to construct by the IT data modelers. However given the complexity of an enterprise view of the data as you go from data mart implementation to data mart implementation retrofitting is significantly harder to accomplish for this architecture. That is why the CIF architecture places the star schema designs in the data marts only never in the data warehouse itself. Functionality The multidimensional architecture provides an ideal environment for relationally oriented multidimensional processing ensuring good performance for complex slice and dice drill-up -down and -around queries. All dimensions are equivalent to each other meaning that all queries within the bounds of the star schema are processed with roughly the same symmetry. We recommend that it be used for the majority of CIF data mart implementations. But do remember that multidimensional modeling does not easily accommodate alternate methods of analysis such as data mining and statistical analysis. The CIF uses a data model that is based on an ERD methodology that supports the business rules of the enterprise. This type of model is also easily enhanced or appended if need be. Attributes are placed in the data model based on their inherent properties rather than specific application requirements. This is an important differentiator in the BI world because it means that the data warehouse is positioned to support any and all forms of strategic data analyses not just multidimensional ones. Data mining statistical analysis and ad hoc or .