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Figure 4–74 Using a Fact Property Value that is not in the Allowed in Actions for Associated Bucketset
4.11.2 How to Use a List of Values Bucketset as a Constraint for a Fact Property
You can use a list of values bucketset as a constraint for a fact property. For more information on using a list of ranges bucket set as a constraint, see
Section 4.11.1, How to Use a List of Ranges Bucketset as a Constraint for a Business Term
.
To use a List of Values bucketset as a constraint for a fact property: 1.
Specify an LOV bucketset that includes the values you want to include, and select Allowed in Actions
for all valid values. For more information, see Section 3.6.1,
How to Define a List of Values Global Bucketset .
2. Deselect Allowed in Actions for the otherwise bucket.
3. Deselect Include Disallowed Buckets in Tests.
4. Associate this bucketset with a fact property.
4.11.3 How to Use Bucketsets to Provide Options for Test Expressions
You can use LOV bucketsets to provide options for expressions and actions.
Working with Rulesets and Rules 4-63
How to use bucketsets to provide options for rule expressions and actions: 1.
In Rules Designer, define an LOV bucketset of a type corresponding to a fact property. For more information, see
Section 3.6.1, How to Define a List of Values Global Bucketset
.
2.
Associate the bucketset with a fact property. For more information, see Section 3.7.1, How to Associate a Bucketset with a Fact Property
.
3.
When you enter expressions, Rules Designer shows the bucket values in the values options. For example, when you associate a fact property Driver.eye_test
with an LOV bucketset named eyes, with values: pass, fail, and glasses_required
, and then you use Driver.eye_test in a test expression, the bucket values are limited as shown in
Figure 4–75 .
Figure 4–75 Using a Bucketset to Provide Options for a Rule Test Expression
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5
Working with Decision Tables 5-1
5
Working with Decision Tables
Using a Decision Table you can create and use business rules in an easy to understand format that provides an alternative to the IFTHEN rule format. The Decision Table
format is intuitive for business analysts who are familiar with spreadsheets. The formal structure that a Decision Table provides makes it easier to author, understand,
and change multiple similar rules and lets software check for rule completeness and consistency.
This chapter includes the following sections:
■
Section 5.1, Introduction to Working with Decision Tables
■
Section 5.2, Creating Decision Tables
■
Section 5.3, Performing Operations on Decision Tables
■
Section 5.4, Creating and Running an Oracle Business Rules Decision Table Application
5.1 Introduction to Working with Decision Tables
Businesses invest in software to automate their business processes. Historically, this automation focused on the collection, presentation, and manipulation of data to
facilitate human decision-making about that data. Increasingly, however, software designers and developers are called upon to automate the decision making process by
putting detailed rules about business processes into software architectures. In addition, many enterprises are experiencing increasing pressure to make software
systems more responsive to business changes. In some cases, the role of writing and testing business rules is no longer assigned to software engineers, but is passed to
trained business users. Alternatively, some organizations attempt to separate changes in the business behavior of software from the traditional software development cycles,
and tie changes to business driven imperatives like product or sales cycles.
A Decision Table provides a mechanism for describing data processing tasks, especially when that description is done by business analysts rather than computer
programmers.
Oracle Business Rules Decision Tables provide the following features:
■
Powerful Visualization: Compact and structured presentation. This visualization matches the way real world business policies are expressed: with many tables,
declarative, and organized into simple steps.
■
Error Prevention: Avoids incompleteness and inconsistency. Because a Decision Table is well structured, automated tools can check for conflicts, redundancy, and
incompleteness to speed development of valid, consistent business rules.
