Sample Projects

From FlexRule Wiki
Jump to: navigation, search

At the end of each sample, there is a link to download the complete project. Each project has a folder named sampleJSON which contains sample inputs.

Info2.png Click here to see the sample projects based on industry.

Business Decision and Rule Management System

This tutorial will use the scenario of checking the eligibility of a pilot as a means of scheduling flight duties.
There are rules and regulations defined by the aviation authority, such as the maximum flight period, the maximum number of night shifts that a pilot can work, etc.
For example, the maximum duty period should be 12.5 hours.
The process contains a set of important steps.
- Calculate the daily flight duty period
- Count the number of landings
- Calculate flight time
- Check the eligibility of pilot on duty
Create a simple Decision Table that calculates delivery costs based on the total weight of a parcel. In the post office, this is usually based on a couple of conditions and business rules, but for the sake of this tutorial, let's make it simple, as shown in the business rules below:
- If is Package between 0 and 22 kg then the delivery cost is 30$
- If is Package between 22 and 50 kg then the delivery cost is 33$
- If is Package between 50 and 110 kg then the delivery cost is 120$
- If is Package is heavier than 110 kg then the delivery cost is 200$
Defining rules to validate the names and addresses of an address book. Natural Language was used to write business rules.
This is a sample for the car insurance industry that calculates the premium of a car based on Auto and Base premium components. The business rules are for risk classification and calculation of the premium of a car.
Using Natural Language, validate the input and finally decide the pass-fail state of a student. In the end, it will create the JavaScript code for the Natural Language code and can be tested in the JavaScript Testbed.
The process of determining the eligibility of providing firearm licenses contain a set of serious rules that require 100% accuracy as it is related to security. The example will show how FlexRule can be used to automate this process.
  1. Read data from the previous applicant list
  2. Check whether the applicant is already applied
  3. For previous applicants, get the previous score
  4. For new applicants, calculate the score
  5. Determine eligibility
The process of approving a building plan contains a set of step according to building type, building size, number of stories, etc. The example shows how to use FlexRule Decision tables to write rules to automate this process.

Decision-Centric RPA (Robotic Process Automation)

Automate the process of a Google-search. It will search for a value in Google and filter the search results to get the list of links on the first page.
- Open a web Browser (eg: Chrome, Firefox)
- Navigate to 'www.google.com'
- Type 'Flexrule' in the search bar
- Press 'Enter' key
- Copy each link one by one
- Close the web browser
Automate the process of reading data from an Excel file to get student records, calculate the total, average, and pass/fail state and write back to the file.
- Open the Excel sheet where you have saved student scores
- Calculate the Total
- Calculate the average
- Depending on the average determine pass/fail state
- Save the Excel sheet with a defined file name
Automate the process of writing a value in MS Paint and save the image file.
- Open MS Paint
- Click on the Text icon
- Click on the canvas where you want to type
- Type the text
- Save the file with a given name

Decision-Centric Workflow

Automate the workflow of the loan approval process
- Collect the applications
- Validate applications
- Check eligibility according to the bureau's standards
- Determine loan application routing
- Team review if required
- Determine whether the loan is approved

Decision-Centric Data & Analytics

This example takes a dataset of weather details to train the model and finally, predicts the possibility of playing golf. It uses the C45 algorithm for predictions. It will:
- Input data
- Train
- Predict
This example takes a dataset of symptoms to train the model and finally, diagnose whether the patient has cold, flu or allergies. It uses the Naive Bayes algorithm for predictions.
- Input data
- Train
- Predict
The data flow and ETL (Extract, transform, load) process were automated using the IRD (Information Requirement Diagram).
The example will get details of a list of bank accounts and determine/calculate the following facts.
  • Maximum balance
  • Minimum balance
  • Average of bank accounts
  • Summation of balances
  • Find the latest and oldest Bank accounts by Account opened date

RPA and Business Rules

Check the eligibility of a supplier. The supplier assessment will perform both a qualitative and quantitative evaluation and help to get the final decision. It will:
- Check the business registration number validity
- Check online whether the company is registered in the business registry
- Give a score depending on different criteria and calculate the final score
- Decide whether the supplier is eligible