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Conditions are modeled using a factless fact table. The star associates dimensions with one another to represent conditions at a particular point in time, or for a period of time. A factless fact table for customer assignments is shown in Figure 12-5. The customer_assignment_facts fact table contains one row for each assignment of a salesperson to a customer. These two dimensions are represented by the salesrep_key and the customer_key. The fact table also had two day_keys. One represents the day on which the assignment became effective; the other represents the day on which it expired. The day dimension will include a special row for unexpired assignments. For current assignments, the day_key_expired will refer to this special row. It will contain the largest date value supported by the DBMS, rather than a NULL or blank. (For more on special rows in dimension tables, see 6, More on Dimension Tables. ) TIP Conditions, coverage, and eligibility should be modeled as factless fact tables. To some readers, customer_assignment_facts does describe a business activity: the activity of assigning salespeople to customers. Each assignment can be thought of as a transaction. Viewed in this light, the design characteristics of the customer_assignment_ facts are not much different from contact_facts. Each captures information about events one about the assignment of customers and the other about the contact with customers. Whether you choose to make a semantic distinction between factless fact tables that describe events and factless fact tables that describe conditions, you are likely to notice a difference in their usage. A fact table that describes conditions will primarily be used to compare those conditions with other activities.

Typically, enterprise applications store, compare, and perform arithmetic on date values. JEE applications typically persist date and time values to a JDBC data store using java.sql.Date; hold them in memory using java.util.Date; manipulate and

This system stored procedure reads e-mail messages from the inbox, executes the queries specified in them, and returns a result set to the sender and all recipients specified on the CC list. It is usually used internally within a job that is periodically executed on the SQL Server. The following statement can be placed in the Job Scheduler and executed periodically to process e-mail that contains the string 'Asset' in the subject against the Asset database. A result set is returned in the form of a comma-separated value (CSV) attachment file:

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In most dimensional schemas, the bulk of changes to source data generate type 1 and type 2 changes. Occasionally, neither technique satisfies. A third type of change response is called into play when there is a need to analyze all facts, those recorded before and after the change, with either the old value or the new value. Neither type 1 nor type 2 does the job here. Solving this problem by storing natural keys in the fact table renders queries difficult to write and opens the door to serious double-counting issues. The preferred approach is to include two attributes for the changed data element, one to carry the current value and one to carry the prior value. Both are updated when a change occurs. Known as a type 3 solution, this allows either to be used to study the entire history.

interpret them using the java.util.Calendar class; and parse, format, and present them using the java.text.DateFormat class. The java.text.DateFormat class is an abstract class that provides a locale-sensitive API for parsing, formatting, and normalizing dates for presentation. The java.text.SimpleDateFormat class implements simple date and time value formatting for all supported locales.

During the course of planning the appropriate slow change response for each column in a dimension table, you will occasionally identify an attribute with two seemingly contradictory requirements: The ability to analyze all facts, recorded before and after the change occurred, using the new value The ability to analyze all facts, before and after the change occurred, using the old value In this situation, neither a type 1 nor a type 2 response is sufficient to keep everyone satisfied This is best understood in the context of an example Suppose that a business measures orders by customer, along with time and several other dimensions When senior management studies orders, they usually do not dive right into customer detail Instead, they use some standard regional groupings.

As mentioned, collation is the process of ordering text using language- or scriptspecific rules, rather than using binary comparison; it is therefore locale-specific. It is possible for a character set to have more than one collating sequences. These lists of characters may be ordered numerically or lexically. For an internationalized application, the abstract class java.text.Collator is recommended for use when ordering lists of items. The java.text.Collator class and its related classes provide collation facilities that are locale-aware. For example, a component that produces ordered lists of NASDAQ stock issue entries could use Collator to place the entries in an order appropriate to the client s locale. You could rely on the database to provide this collation. However, this may not be a good idea for internationalization, because most databases support a single sort order (typically specified at installation) and may not be portable to another database vendor.

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