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FULL INDEX
Mike Parish edited this page Sep 17, 2023
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- Abstract Decision Tables
- Alternate Representation for Resource Allocation
- Alternative Decision Structures using included function libraries
- Alternative Decision Structures using included function libraries
- Option 1 Inputs and Outputs explicitly defined as objects
- Option 2 Inputs and Outputs specified as Functions (rather than objects)
- Option 3 No Explicit Inputs or Outputs - Data read/written using functions inside decision tables
- Option 4 Database Query Strings passed as Inputs
- Automated Test Data Generation
- Automated Test Data Generation
- BizVR Discussion
- BizVR Tutorial
- BizVR Basic Tutorial - How to create your first decision model and decision table
- Business Problem
- Modeling the Decision in a Formal Editor
- Business Decisions
- Case Study Transportation Scheduling
- Case Study Transportation Scheduling Approach 2 Functions
- Case Study Transportation Scheduling Approach 3 Methods and Polymorphism
- Case Study Transportation Scheduling Approach 4 Pure Functions
- Category A A Example
- Category A->A Example (NEEDS REVISION)
- Approach 1 - Standard DTs
- Generate Toys instances
- Define Colors, Sizes and Shapes
- Approach 2 - Functions
- Approach 3 - Collections as Input
- Approach 4 - Collection Cross Product As Input
- Category s A Example
- Category s->A Example
- Category s s Example
- Category s->s Example
- Category Theory and Decision Tables
- Category Theory and Decision Tables
- Some Categories of Decision Tables
- s->s Example
- s->A Example
- A->A Example
- A->B Example
- AxA->A Example
- AxA->B Example
- AxB->C Example
- Category Theory definition
- Category Theory definition
- Checking the completeness of the rules
- Checking the completeness of the rules
- Checking the consistency of the rules
- Checking the consistency of the rules
- Conflicts Between Rules in Different Tables
- Conflicting Actions
- Conflicting Conditions
- Checking the rules for constraint violations
- Checking the rules for constraint violations
- Checking the rules for redundancies
- Checking the rules for redundancies
- Checking the rules for values set and tested
- Checking the rules for values set and tested
- Classes Of Decision
- Classes Of Decision
- Collection = function of collection(s)
- Collection = function of collection(s)
- Collections
- Collections
- Basic Collections
- Collections
- Sets
- Lists
- Operations on Collections
- Basic Collection Operators
- Single Collection operators
- Sequenced Collection operators
- Multiple Collection operators (Collections are of the same type)
- Multiple Collection operators (Collections may be of different type)
- Attribute specific operators:
- Filters (produce subsets of a collection)
- Complete Examples with Execution
- Complete Examples with Execution
- Complex Decision Example
- Complex Decision Example
- Composition of Methods
- Composition of Methods Example
- Sample Business Problem
- Approach 1 Applying Methods In Separate Steps
- Approach 2
- Approach 3
- Approach 4
- Context Bindings
- Context Bindings
- Data Functions
- Date Functions
- Decimal Functions
- Decimal Functions
- Decision Management Community Rule Modeling Challenge Problems
- Decision Management Commmunity Rule Modeling Challenge Problems
- Decision Model Structure
- Decision Model Structure
- Decision Ontology
- Decision Ontology
- Decision Table Dependency Rules
- Decision Table Dependency Rules
- DEFINITION
- Four Cases To Consider
- NOTES
- Basic Dependency Principles
- Object Dependency
- Function Dependency
- The Independent Table
- Decision Table Keywords
- Decision Table Keywords
- Decision Table Semantics
- Semantics
- condition
- Examples:
- conditionValue
- Examples:
- action
- Examples:
- actionvalue
- Decision Table Syntax and Semantics
- Decision Table Syntax and Semantics
- Decision Table Views
- Decision Views
- Example
- Views Based On Color
- When color is 'A'
- When color is 'B'
- When color is 'C'
- Views Based on Size
- When size is 1
- When size is 2
- When size is 3
- Views Based on Shape
- When shape is 'X'
- When shape is 'Y'
- Views Based on Price
- When price is 'p'
- When price is 'q'
- When price is 'r'
- Decision Tables and Collections
- Decision Tables and Collections
- Decision Level Filtering
- Rulesheet Level Filtering
- Hierarchical Collections
- Collection Pattern Matching
- Collection Expressions
- Decision Tables as Class Methods
- Decision Tables as Class Methods
- Decision Tables as Composed Methods
- Decision Tables as Composed Methods
- Comparison of Approaches
- Decision Tables as Functions
- Decision Tables As Functions
- Example 1
- Example 2
- Decision Tables as Instance methods
- Decision Tables as Instance methods
- Decision
- Decision Table
- Function
- Method
- Decision Tables Rules about different Types (*)
- Decision Tables Rules about different Types
- Decision Trees
- Decision Trees
- Decisions about Decisions (Reflective Decisions)
- Reflective Decisions (Decisions about Decisions)
- Traversing the workspace
- Decisions as Pure Functions (no side effects)
- Decisions as Pure Functions (no side effects)
- Function f(x,y) defined as a decision table:
- Function g(x) defined as a decision table:
- Function h(y) defined as a decision table:
- Validation
- Using Instance Methods
- Defining Abstract Decision Tables
- Defining Abstract Decision Tables
- Defining Constraints in Decision Tables
- Defining Constraints in Decision Tables
- Example
- Defining Database Access Using Decision Tables
- Defining Database Access Using Decision Tables
- Defining Event Processing Using Decision Tables
- Defining Event Processing Using Decision Tables
- Defining Functions as Decision Tables
- Defining Functions as Decision Tables
- Defining Grammars Using Decision Tables
- Defining Grammars Using Decision Tables
- Defining Graphs and Graph Processing Logic Using Decision Tables
- Defining Graphs and Graph Processing Logic Using Decision Tables
- Defining Interactive Dialogs Using Decision Tables
- Defining Interactive Dialogs Using Decision Tables
- Defining Logic Programming Using Decision Tables
- Defining Logic Programming Using Decision Tables
- Defining Lookup Tables as Decision Tables
- Defining Lookup Tables as Decision Tables
- Requirements
- Defining Map Reduce Using Decision Tables
- Defining Map/Reduce Using Decision Tables
- Defining Monitoring Logic Using Decision Tables
- Defining Monitoring Logic Using Decision Tables
- Defining Multi Dimensional Spreadsheets as Decision Tables
- Defining Ontologies Using Decision Tables
- Defining Ontologies Using Decision Tables
- Entity Tables and Association Tables
- Defining Optimization Logic Using Decision Tables
- Defining Optimization Logic Using Decision Tables
- Defining Parallel Execution Using Decision Tables
- Defining Parallel Execution Using Decision Tables
- Defining Pattern Matching Logic as Decision Tables
- Defining Pattern Matching Logic as Decision Tables
- Defining Quantum computations using decision tables
- Defining quantum computations using decision tables
- Defining RDF Triple Processing Using Decision Tables
- Defining Decision Tables for RDF Triple Processing
- Defining Test Values as Decision Tables
- Defining Test Values as Decision Tables
- Different Types of Decision Tables
- Types of Decision Table
- Documents for Download
- Dynamic Questionnaires in Decision Tables
- Dynamic Questionnaires in Decision Tables
- Example 1 Asking for Decision Inputs
- Example of a Decision Using Functions and Methods
- Example of Decisions using Functions and Methods
- Main Decision Table - Determines Tax
- Function to Determine Price
- Method to Determine Size
- Examples of Decisions
- Executing BizVR Decision Models in Test Mode (RGB)
- Executing BizVR Decision Models in Test Mode
- How to check the results automatically
- Expressions
- Expressions
- Functional Programming vs. Object oriented Programming
- Functional Programming vs. Object-oriented Programming
- Functions and Methods
- Morphisms
- How do I...
- How do I.....
- How to Create New Instances of a Class
- How to Create New Instances of a Class
- How to edit inputs and outputs
- How to edit inputs and outputs
- 1. Let's now return to the Inputs and Outputs
- 2. Click on this symbol to edit the decision meta data
- 3.and then select the Input/Output tab
- 4. Click + to add inputs and outputs and enter the following
- 5. Save and return to the decision diagram
- INDEX
- Index 2023 08 10
- Index 2023 08 17
- Index Summary
- Inheritance and Specialization of Methods and Functions
- Inheritance and Specialization of Methods and Functions
- Integer Functions
- Integer Functions
- Jan 2018 Order Promotions
- The Problem
- BizVR Solution
- Adding More Rules
- Methodology for Modeling Decisions
- Methodology for Modeling Decisions
- Identify the decision being made.
- Identify the factors that affect the decision.
- List in English the rule statements that make the decision
- Identify the business objects, attributes and data types of these factors.
- Customer has
- Property has
- Coverage has
- Develop a formal vocabulary that defines the business objects, factors, attributes and types
- Define the relationships between the various business objects
- Identify the sequence of steps involved in making the decision
- Group the rules into rule groups.
- Enter all of the English rule statements that apply to each rule group.
- Enter the natural language for each condition and action in the rule statement
- Enter an expression that implements each of the natural language statements
- Check the rules for ambiguity
- Check the rules for completeness
- Create a test case for each rule group on its own.
- Create a test case for every rule in your rule group.
- Once rule groups are created and tested you can start assembling them into decisions.
- Create test cases for the entire decision.
- MEP Thoughts 2021
- Miscellaneous Documents for Download
- Modes of Execution
- Modes of Execution
- Object = function of object(s)
- Object = function of object(s)
- Object = function of values(s)
- Object = function of values(s)
- Passing Arguments to Methods and Functions
- Passing Arguments to Methods and Functions
- Example
- Positional Approach - arguments are comma separated values
- Keyword Approach - arguments are passed along with their name
- Permutations and Combinations
- Permutations and Combinations
- FUNCTION:combinations(n,r)->number
- FUNCTION:permutations(n,r)->number
- Robustness
- FUNCTION: factorial(n)
- Processing Collections With Decision Tables
- Processing Collections With Decision Tables
- Prolog Examples
- Prolog Examples (using Visual Prolog)
- Example 1
- Example 2
- RDF
- RDF
- Recursive Decision Table Functions
- Recursive Decision Table Functions
- Example 1 Factorial
- Example 2 Fibonacci
- Example 3 Partitions
- Resource Allocation Alternative Approaches
- Resource Allocation Alternative Approaches
- Resource Allocation Case Study
- Resource Allocation Example
- The Problem
- The Decision Model
- Decision Table Solution using Functions
- Execution
- INPUT JSON:
- OUTPUT JSON:
- Resource Allocation Example Using BizVR Decision Tables
- Resource Allocation Example Using BizVR Decision Tables
- The Business Problem
- Resource Allocation Flat Data Model Standard Decision Tables
- Resource Allocation Flat Data Model Standard Decision Tables (No functions, no methods)
- Data Model
- Decision Model
- Decision Tables
- Allocate Resources to Tasks
- Determine Cost
- Determine Duration
- Resource Allocation Flat Data Model Using Functions and Methods
- Resource Allocation Flat Data Model Using Functions and Methods
- The Data Model
- The Decision Model
- The Decision Tables
- The Allocate Function
- The Cost Method for Resources
- The Duration Method for Tasks
- Resource Allocation Functional Model
- Resource Allocation Inheritance and Polymorphism Model
- Data Model
- Decision Model
- Decision Table Allocate
- RGB Case Study
- Rule Development Methodology
- Rule Development Methodology
- Identify the decision being made.
- Identify the factors that affect the decision.
- List in English the rule statements that make the decision
- Identify the business objects, attributes and data types of these factors.
- Customer has
- Property has
- Coverage has
- Develop a formal vocabulary that defines the business objects, factors, attributes and types
- Define the relationships between the various business objects
- Identify the sequence of steps involved in making the decision
- Group the rules into rule groups.
- Enter all of the English rule statements that apply to each rule group.
- Enter the natural language for each condition and action in the rule statement
- Enter an expression that implements each of the natural language statements
- Check the rules for ambiguity
- Check the rules for completeness.
- Create a test group for each rule group on its own.
- Create a test case for every rule in your rule group.
- Once rule groups are created and tested you can start assembling them into decisions.
- Create test cases for the entire rule flow.
- MEP Thoughts 2021
- Rule Validation
- Rule Validation
- Semantic Indexing of Documents
- Semantic Indexing of Documents
- Sep 2015 Collections of Cars
- Collections of Cars Solutions
- Introduction
- Part I Association Model
- The Vocabulary
- The Rule Sheet
- setting default price with exceptions covered by explicit rules
- String Functions
- String Functions
- Testing Example
- Testing Example
- TLP Insurance Case Studies
- TLP Insurance Case Studies
- TLP Insurance Combined Decision and Ontology
- TLP Insurance Credit Scoring Sub Decision
- TLP Insurance Inheritance
- TLP Insurance Ontology Only
- TLP Insurance Using Credit Scoring Sub Decision
- TLP Insurance Using Functions Case Study
- Case Study TLP Insurance Basic Decision Model Using Functions
- Coverage Table
- Properties Table
- Customer Table
- Sample Execution
- TLP Insurance Using Inferencing Case Study
- Case Study TLP Insurance
- Overall Risk Decision Tables
- Coverage Risk Decision Table
- Property Risk Decision Table
- Customer Financial Risk Decision Table
- Toy Example
- Applying Methods
- Composing Methods
- Defining Composed Methods (not sure this is the best example - mep)
- Transportation Scheduling Execution Example
- Transportation Scheduling Execution Example - Functions
- Tutorial Creating Decision Table Functions
- Tutorial Creating Decision Table Functions
- Tutorial Using The Validation Tool
- Tutorial Using The Validation Tool
- Tutorial Automating test data generation
- Automating test data generation
- Tutorial Automating the checking of results
- Automating the checking of results
- Discussion Point
- Tutorial How to test a decision model
- Tutorial How to test a decision model
- 1. Click on the decision table and change its name to this:
- Now edit the inputs and outputs to look like this:
- 2. To do this, exit to this screen
- 3. Click on this symbol to edit the decision meta data
- 4.and then select the Input/Output tab
- 4. Click + to add inputs and outputs and enter the following
- 5. Save and return to the decision diagram
- Enter the following json for your input values (or just copy/paste).
- Tutorial Some Examples of JSON
- Tutorial Some Examples of JSON
- Tutorial 1: Three unrelated properties (color, size and shape)
- Tutorial 2: single object named "toy" with three properties
- A collection (named TOYS) consisting of one or more toy (as defined above)
- A collection named CHILDREN each of which has a name and a collection of TOYS
- A Collection of colors
- A Collection of the nine digits from 1 thru 9
- Santa's Reindeer
- A Collection of PERSONS
- CUSTOMERS, ORDERS and ITEMS
- Tutorial Testing with a collection of inputs
- Testing with a collection of inputs
- Tutorial Topics Summary
- Tutorial Topics
- Unification (i.e. Prolog)
- Unification (i.e. Prolog)
- Using OTHER in rules
- Using OTHER in rules
- Method 1 Add explicit rules for the missing combinations based on suggestions from the validation engine:
- Method 2 Use the keyword OTHER to represent anything missing:
- Method 3 Set a default value for group
- Validation Engine as a standalone Aion app
- Validation Engine as a standalone Aion app
- Using the BizVRValidatorGUI
- Value = function of Collection(s)
- Value = function of Collection(s)
- Value = function of object(s)
- Value = function of object(s)
- Value = function of value(s)
- Value = function of value(s)
- Visualizing the Execution of Decision Models
- Visualizing the Execution of Decision Models
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