FULL INDEX

• Home
• • BizVR Business Language
• • What is BizVR
• • Introduction
• Abstract Decision Tables
• • Abstract Decision Tables
• • The Business Problem
• • The BizVR Solution
• Alternate Representation for Resource Allocation
• • Alternate Representation
• • NOTES
• 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
• • 1. Click on this URL http://gnorion.com:3202/main and you should see this screen:
• • 2. Enter your user id and password (or choose guest mode if you do not have one)
• • 3. Select BizVR Examples Partition in the left column and then click + to create a new Workspace
• • 4. Give your workspace a unique name (other fields don't work yet).
• • 5. Click the little cloud symbol to save your workspace (be careful not to click the trash can or you will delete it)
• • 6. Your workspace should show up in the list
• • 7. Now click + to create a project in your workspace, name it and save it.
• • 8. It will show up like this
• • 9. Click to open the project
• • 10. Click + to create a decision, name it and save it
• • 11. Click the + to create a new decision table and name it
• • 12. Now click on this decision table to enter its details (you can also add more empty decision tables and give them names)
• • 13. Click to get into edit mode
• • 14 use the up/down arrows to add two more rows in the top part of the table (the condition section)
• • 15. Now enter color, size and shape as the three conditions and price as the single action and save the decision table
• • 16. Now click the + symbol three times to add three rule columns
• • 17. Now complete the table by filling in the rules. Don't forget to save it
• • You should now see this
• Business Decisions
• • What is a Business Decision
• Case Study Transportation Scheduling
• • Case Study - Transportation Scheduling
• • Problem Statement
• • Different Approaches
• • Approach 1 Standard Decision Tables with Inferencing
• • Approach 2 Functions and Standard Decision Tables
• • Approach 3 Methods and Polymorphism and Standard Decision Tables
• • Approach 4 Every Decision Table is Implemented as a Function
• • Approach 5 Every Decision Table is Implemented as a Method
• • Case Study Transportation Scheduling Approach 1 Standard Decision Tables with Inferencing
• • Case Study Transportation Scheduling Approach 1 Standard Decision Tables with Inferencing
• Case Study Transportation Scheduling Approach 2 Functions
• • Case Study Transportation Scheduling Approach 2 Functions
• • Function to check that the driver meets any customer specific requirements
• • Function to check that the driver meets all the mandatory requirements
• Case Study Transportation Scheduling Approach 3 Methods and Polymorphism
• • Case Study Transportation Scheduling Approach 3 Methods and Polymorphism
• Case Study Transportation Scheduling Approach 4 Pure Functions
• • 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