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Data Modeling Techniques for Data Warehousing phần 8

mô hình chiều thường có xu hướng trở nên phức tạp và dày đặc. Điều này có thể gây ra vấn đề cho người dùng cuối. Để giải quyết vấn đề này, hãy xem xét xây dựng các mô hình dữ liệu hai tầng, trong đó tầng back-end bao gồm tất cả các tạo tác mô hình và cơ cấu đầy đủ của | from it usually is an ideal solution. The simpler local time dimensions also are more suitable for being flattened into a time dimension table. In this way the performance and querying capabilities of the total solution are further maximized. Notice that in the absence of a corporatewide time dimension every end-user group or every department will develop its own version of the time dimension resulting in unlike meanings and different interpretations. Because time-related analysis is done so frequently in data warehouse environments such situations obviously provide less consistency. Lower Levels of Time Granularity Depending on specific business organization aspects and end-user requirements the granularity of the time dimension may have to be even lower than the day granularity that we assumed in the previously developed examples. This is typically the case when the business is organized on the basis of shifts or when a requirement exists for hourly information analysis. 8.4.4.3 Modeling Slow-Varying Dimensions We have investigated the time dimension as a specific dimension in the data warehouse and have assumed that dimensions are independent of time. What we now need to investigate is how to model the temporal aspects in the dimensions of the dimensional data model. Dimensions typically change slowly over time in contrast to facts which can be assumed to take on new values each time a new fact is recorded. The temporal modeling issues for dimensions are therefore different from those for facts in the dimensional model and consequently also the modeling techniques commonly referred to as modeling techniques for slow-varying dimensions. When considering slow-varying dimensions we have to investigate aspects related to keys attributes hierarchies and structural relationships within the dimension. Key changes over time are obviously a nasty problem. Changes to attributes of dimensions are less uncommon but special care has to be taken to organize the model well so