lịch sử, trong khi kho dữ liệu nên có thể chụp 3-5 thậm chí 10 năm lịch sử, về cơ bản cho tất cả các dữ liệu được ghi lại trong kho dữ liệu. Trong một ý nghĩa, một cơ sở dữ liệu lịch sử là một cơ sở dữ liệu chiều, kích thước được thời gian. | history whereas data warehouses should be able to capture 3 to 5 to even 10 years of history basically for all of the data that is recorded in the data warehouse. In one sense a historical database is a dimensional database the dimension being time. In that sense a historical data model could be developed using a dimensional modeling approach. In the context of corporate data warehouse modeling building the backbone of a large-scale data warehouse we believe this makes no sense. In this case the recommended approach is an ER modeling approach that is extended with time variancy or temporal modeling techniques as described earlier in this chapter. There are two basic reasons for the above-mentioned recommendation Corporate historical models most often emerge from an inside-out approach using existing OLTP models as the starting point of the modeling process. In such cases reengineering existing source data models and integrating them are vital processes. Adding time to the integrated source data model can then be considered a model transformation process suitable techniques for doing this have been described in various sections of this chapter. Historical data models can become quite complicated. In some cases they are inherently unintuitive for end users anyway. In this case one of the basic premises for using dimensional modeling simply disappears. Notice that this observation implies that end-users will find it difficult to query such historical or temporal models. The complications of a historical data model will therefore have to be hidden from end users using tools or two-tiered data modeling or an application layer. A modeling approach for building corporate historical data models basically consists of two major steps. The first step is to consolidate existing source data models into a single unified model. The second step is to add the time dimension to the consolidated model very much according to the techniques described in Temporal Data Modeling .
