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The last dimensional feature upon which business intelligence tools may have an impact is the bridge table. As you learned in s 9 and 10, bridge tables allow a single fact to refer to more than one instance of a dimension. A bridge table exploits the Cartesian product that is created when the dimension is placed in a many-to-many relationship with the fact table. Although this provides powerful analytic possibilities, it also introduces the potential for misuse. The potential for double-counting when using a bridge table can be compensated for by creating an alternative view of the schema for novice users. Like many of the other techniques in this chapter, that may be accomplished using views, or by taking advantage of features that are built into your business intelligence product. This safe configuration for the novice must be accompanied by a more flexible, albeit dangerous, solution for the experts.

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The "all-or-nothing" commitment that a DBMS makes for the statements in a transaction seems almost like magic to a new SQL user. How can the DBMS possibly back out the changes made to a database, especially if a system failure occurs during the middle of a transaction The actual techniques used by brands of DBMS vary, but almost all of them are based on a transaction log, as shown in Figure 12-5.

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A dimension bridge table allows a fact table to be associated with more than one row in a dimension table. This allows modeling of a multi-valued dimension one that can take on more than one value for a given fact. The technique is very flexible, allowing an indefinite number of dimension instances to be associated with each fact. This permits the development of impact reports, which associate the same fact with each member of the dimension. The bridge table sales_goup, at the top of Figure 16-9, permits any number of salesreps to be associated with each order. A group_key is assigned for each collaboration, and rows are added to the sales_group table for each salesrep who contributed. This allows the fact table to carry a single group_key, regardless of the number of salespeople involved. When the sum of order_dollars is grouped by salesperson_name, the value will be repeated for each salesrep who participated in the sale. This kind of report illustrates the involvement of each salesrep in the orders process. It is known as an impact report, since it would not be accurate to create a grand total from the results. Proper use of the bridge table avoids aggregating the fact across members of the group. There are three ways to avoid doing so: 1. Group results by dimension rows (generally with the natural key) 2. Constrain the query for a single member of the dimension (again using a natural key) 3. Constrain the query to isolate a single member of each group

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Codd's article triggered a flurry of relational database research, including a major research project within IBM. The goal of the project, called System/R, was to prove the workability of the relational concept and to provide some experience in actually implementing a relational DBMS. Work on System/R began in the mid-1970s at IBM's Santa Teresa laboratories in San Jose, California. In 1974 and 1975 the first phase of the System/R project produced a minimal prototype of a relational DBMS. In addition to the DBMS itself, the System/R project included work on database query languages. One of these languages was called SEQUEL, an acronym for Structured English Query Language. In 1976 and 1977 the System/R research prototype was rewritten from scratch. The new implementation supported multi-table queries and allowed several users to share access to the data. The System/R implementation was distributed to a number of IBM customer sites for evaluation in 1978 and 1979. These early customer sites provided some actual user experience with System/R and its database language, which, for legal reasons, had been renamed SQL, or Structured Query Language. Despite the name change, the SEQUEL pronunciation remained and continues to this day. In 1979 the System/R research project came to an end, with IBM concluding that relational databases were not only feasible, but could be the basis for a useful commercial product.

7. Click OK to accept these settings. Now, you simply need to save the settings (File | Save). For this example, name the file ntconfig.pol and make a note of where you saved it. Now that we ve defined the system policies, it s time to apply them to clients.

SALES_GROUP sales_group_key salesrep_key primary_flag SALESREP salesrep_key salesperson_name region . . .

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