Lecturer: Prof. Dr. Dimitris Karagiannis, Prof. Dr. Alfred Taudes

Description

This course is an introduction to intelligent information processing and knowledge processing. All aspects of business intelligence, e-business intelligence and relevant portions of knowledge management are taught. Both general business-intelligence technologies and technologies related with business intelligence are introduced. A wide range of so-called softcomputing technologies is investigated. Theoretical concepts are explained and deepened by practical examples and business intelligence tools. Students learn to understand the various approaches to intelligent information and knowledge processing and to detect promising applications where business-intelligence technologies can be used.

The first part of the course illustrates the history of business intelligence and e-business intelligence. Important definitions are given. Business intelligence technologies like data warehouses, OLAP and data mining are investigated. Furthermore we study applications and concepts related with business intelligence: Supply chain management, customer relationship management, balanced scorecard, enterprise information portals, and XML (eXtensible Markup Language). Fundamentals of knowledge processing are introduced.
The second part of the course covers softcomputing technologies which are used in the context of business intelligence. These technologies are considered part of artificial intelligence (AI). They include, among others, fuzzy logic and fuzzy systems, artificial neural nets and knowledge representation. Basic principles and concepts from these fields are taught. In the final lessons, business intelligence tools by the market leaders - Oracle, IBM, SAP, etc. - are discussed so that students get an overview of the state-of-the-art of commercial systems.

Outline

Part I: Introduction to Business Intelligence

1. Basics: Knowledge, inference, artificial intelligence
2. Business intelligence technologies
   2.1 Data warehouse
   2.2 OLAP
   2.3 Data mining
3. Related technologies and applications
   3.1 Supply chain management, customer relationship management, balanced scorecard, enterprise information portals
   3.2 XML (eXtensible Markup Language)
   3.3 Basics of knowledge processing

Part II: Softcomputing Technologies in Business Intelligence

4. Symbolic artificial intelligence
   4.1 Heuristic search
   4.2 Rule-based systems
   4.3 Frames, semantic networks
   4.4 Languages, knowledge engineering, applications
5. Fuzzy logic and fuzzy systems
   5.1 Fuzzy sets, fuzzy inference, fuzzy control
   5.2 Implementation of fuzzy systems
   5.3 Software tools and applications
6. Neural networks (NN)
   6.1 Definitions and classifications of NN
   6.2 Multi-layer perceptrons, Kohonen networks
   6.3 Backpropagation
   6.4 Software tools and applications
7. Other aspects of knowledge processing
   7.1 Definition and dimensions of knowledge processing
   7.2 Business process and workflow management software
   7.3 Document management systems
   7.4 Content management systems
   7.5 Groupware

Learning Objectives

After completing this course successfully, students can expect to:

1. possess a basic understanding of the fundamental technologies used in the field of business intelligence, and of related concepts and technologies,
2. have solid knowledge of artificial neural nets, fuzzy systems, and knowledge processing,
3. know how to apply business intelligence technologies to satisfy information requirements of an organization,
4. be able to select appropriate technologies and tools depending on the problems to be solved.

Method of teaching

This is a audio-based course ("narrated slide show", type 3). Weekly lectures consist of animated and interactive slide shows, enhanced by audio tracks and text. This combination is easy to use for the students. As a further means to support understanding videos are employed. These videos are especially used to introduce new topics and to provide additional visual explanations when needed. Videos are provided in a compact and easily accessible streaming format (as in type 1 courses).

The course is divided into lectures and exercises. The time for studying the lecture materials and for solving the weekly homework assignment is approximately four to five hours per week. "Lecture" means that course materials have to be studied in weekly lessons. In addition to the slides-based lectures, literature is provided for the students as PDF files for further study of the respective subject in more detail. Interactive tests are available to check the learning progress continuously.

Exercises are given so that students can deepen the subjects covered in the lectures and develop a more thorough understanding. Participants have to solve the assignments on a weekly basis. Depending on the kind of exercise students have to work on their own or in groups. The submitted solutions are graded by the instructors. The final grade depends on the quality of the submitted solutions and the results of an exam at the end of the semester.
Communication (e.g. subsequent discussion of the lecture's topics, questions, answers, exercises etc.) between students and instructors is based on electronic media like discussion forum, bulletin board, chat room, and e-mail. Students are always welcome to ask questions and will receive answers as soon as possible.

Requirements

No formal prerequisites. The same system resources as for other VGU courses are needed.

If you take this course as an individual course, you will obtain a VGU certificate. Prerequisite is that you solve the weekly exercises satisfactorily and pass the final exam.

Credits

For MBI students: 6 credit points