You're reading from AI and Business Rule Engines for Excel Power Users Capture and scale your business knowledge into the cloud – with Microsoft 365, Decision Models, and AI tools from IBM and Red Hat

Product type Paperback

Published in Mar 2023

Publisher Packt

ISBN-13 9781804619544

Length 386 pages

Edition 1st Edition

Languages

Power Query

Tools

Excel

Concepts

Information Management

Authors (2):

Paul Browne (GBP)

ALEX PORCELLI

View More author details

Table of Contents (22) Chapters

Preface

1. Part 1:The Problem with Excel, and Why Rule-Based AI Can Be the Solution FREE CHAPTER

2. Chapter 1: Wrestling with Excel? You Are Not Alone

3. Chapter 2: Choosing an AI and Business Rules Engine – Why Drools and KIE?

4. Chapter 3: Your First Business Rule with the Online KIE Sandbox

5. Part 2: Writing Business Rules and Decision Models – with Real-Life Examples

6. Chapter 4: More Decision Models, Business Rules, and Decision Tables

7. Chapter 5: Sharing and Deploying Decision Models Using OpenShift and GitHub

8. Chapter 6: Calling Business Rules from Excel Using Power Query

9. Part 3: Extending Excel, Decision Models, and Business Process Automation into a Complete Enterprise Solution

10. Chapter 7: Using Business Rules in Excel with Visual Basic, Script Lab, or Office Scripts

11. Chapter 8: Using AI and Decision Services Within Power Automate Workflows

12. Chapter 9: Advanced Expressions, Decision Models, and Testing

13. Part 4: Next Steps in AI, Machine Learning, and Rule Engines

14. Chapter 10: Scaling Rules in Business Central with Docker and the Cloud

15. Chapter 11: Rules-Based AI and Machine Learning AI – Combining the Best of Both

16. Chapter 12: What Next? A Look inside Neural Networks, Enterprise Projects, Advanced Rules, and the Rule Engine

17. Index

Why subscribe?

18. Other Books You May Enjoy

Appendix A - Introduction to Visual Basic for Applications

Introduction to macros and VBA

1. Appendix B - Testing Using VSCode, Azure, and GitHub Codespaces

2. Appendix C - Troubleshooting Docker

Machine-readable web pages using REST

We’ve been talking about machine-readable web pages using REST and JSON as a common language.

REST stands for Representational State Transfer, which is another way of describing machine-readable web pages. Let’s take a look at what that looks like with a simple example, before exploring the Kogito REST API in more detail.

A simple REST example

REST uses a lot of the same infrastructure as browsing the web – so you can open a REST URL such as https://api.github.com/repos/OfficeDev/office-js using a web browser, like in Figure 6.2. This screenshot was taken using Firefox, as it colors the JSON for us, but Edge and Chrome will give you similar results. Note that a lot more information will be returned – we’ve edited the image for clarity.