Tham khảo tài liệu 'managing time in relational databases- p3', công nghệ thông tin, cơ sở dữ liệu phục vụ nhu cầu học tập, nghiên cứu và làm việc hiệu quả | 20 Chapter 1 A BRIEF HISTORY OF TEMPORAL DATA MANAGEMENT Whenever we can specify the semantics of what we need without having to specify the steps required to fulfill our requests those requests are satisfied at lower cost in less time and more reliably. SCDs stand on the wrong side of that what vs. how divide. Some IT professionals refer to a type SCD. Others describe types 0 4 5 and 6. Suffice it to say that none of these variations overcome these two fundamental limitations of SCDs. SCDs do have their place of course. They are one tool in the data manager s toolkit. Our point here is first of all that they are not bi-temporal. In addition even for accessing uni-temporal data SCDs are cumbersome and costly. They can and should be replaced by a declarative way of requesting what data is needed without having to provide explicit directions to that data. Real-Time Data Warehouses As for the third of these developments it muddles the data warehousing paradigm by blurring the line between regular periodic snapshots of tables or entire databases and irregular as-needed before-images of rows about to be changed. There is value in the regularity of periodic snapshots just as there is value in the regular mileposts along interstate highways. Before-images of individual rows taken just before they are updated violate this regular snapshot paradigm and while not destroying certainly erode the milepost value of regular snapshots. On the other hand periodic snapshots fail to capture changes that are overwritten by later changes and also fail to capture inserts that are cancelled by deletes and vice versa when these actions all take place between one snapshot and the next. As-needed row-level warehousing real-time warehousing will capture all of these database modifications. Both kinds of historical data have value when collected and managed properly. But what we actually have in all too many historical data warehouses today is an ill-understood and thus poorly managed .
