Section 1 : AI in business intelligence (BI)

Commentary

Section Goals

• To explore concepts and methods about uncertainty and probability.

Section Notes

• Parts of this section may be skipped by those who have a strong background in probability and Bayesian networks.

Learning Objectives

Learning Objective 1

• Outline the main tasks, components, and tools of business intelligence.
• Discuss the problems and bottlenecks of elevating and upgrading the current business intelligence software systems, and discuss the possible ways of applying AI to solve these problems.

Objective Readings

Required readings:

Reading topics:

(See 'Unit Readings' for information on where to look for the following topics) Business Intelligence, OLAP, Data Warehouse, Data Mining, Decision Support System (DSS).

Supplemental readings:

Witten, I. H., and Frank, E. (2005). Data mining: Practical machine learning tools and techniques (2nd ed.). Morgan Kaufmann.

Objective Questions

• How are BI, DSS, and data mining related?
• How can Bayesian networks, decision networks, and machine learning be used to solve BI problems?

Objective Activities

• Explore the Internet and the AU Digital Library to find papers about business intelligence techniques, solutions, and tools that are of interest to you, and discuss them in the online course conference.