tailieunhanh - Data Modeling Techniques for Data Warehousing phần 6

, hiển thị tất cả các yếu tố của mô hình và các tài sản của mình. Các mô hình chi tiết chiều tiếp tục có thể được mở rộng và tối ưu hóa. Nhiều kỹ thuật trong lĩnh vực này nên được xem như kỹ thuật mô hình tiên tiến. Không phải mọi dự án đòi hỏi tất cả chúng được áp dụng. | Figure 43. Requirements Validation. Requirements Modeling. Validated initial models are further developed into detailed dimensional models showing all elements of the model and their properties. Detailed dimensional models can further be extended and optimized. Many techniques in this area should be thought of as advanced modeling techniques. Not every project requires all of them to be applied. We cover some of the more commonly applied techniques and indicate what other issues may have to be addressed. The major activities that are part of requirements modeling are illustrated in Figure 44. Initial Dimensional Models Analysis Model Informal End-User Requirements Irformation-orientod requirements Process-oriented requirements Integrated Source Data Models ----------------------------- Requirements Modeling Modeling the dimensions non-temporal dimensions time dimension Time-variancy modeling modeling slow-varying dimensions Dimension model optimizations and enhancements Supportive attributes Constraints - Dimension-dimension relationships - Fact consolidations Drill-across Volume assessment Figure 44. Requirements Modeling. When advanced dimensional modeling techniques are used such as the ones indicated in Figure 44 the dimensional model usually tends to become complex and dense. This may cause problems for end users. To solve this consider building two-tiered data models in which the back-end tier comprises all of the model artifacts and the full structure of the model Chapter 8. Data Warehouse Modeling Techniques 91 whereas the front-end tier the part of the model with which the end user is dealing directly is a derivation of the entire model made simple enough for end users to use in their data analysis activities. Two-tier data modeling is not required as such. If end users can fully understand the dimensional model the additional work of constructing the two tiers of the model should not be done. Design Construction Validation and Integration. Once .