Knowledge Engineering and Business Intelligence
News
- February 29th, 2016: The course started!!
General Info
Teachers:
Lessons schedule:
- 29/02/2016, 11am - 1pm and 3pm - 5pm (Teachers, Holger Wache/Knut Hinkelmann)
- 01/03/2016, 9am - 1pm (Teachers, Holger Wache/Knut Hinkelmann)
- 31/03/2016, 9am - 1pm (Teacher, Holger Wache)
- 01/04/2016, 3pm - 7pm (Teacher, Holger Wache)
- 04/04/2016, 11am - 1pm and 3pm - 5pm (Teacher, Holger Wache)
- 05/04/2016, 9am - 1pm (Teacher, Holger Wache)
- 11/04/2016, 11am - 1pm and 3pm - 5pm (Teacher, Knut Hinkelmann)
- 12/04/2016, 9am - 1pm (Teacher, Knut Hinkelmann)
- 18/04/2016, 11am - 1pm and 3pm - 5pm (Teacher, Knut Hinkelmann)
- 19/04/2016, 9am - 1pm (Teacher, Knut Hinkelmann)
Students Office hours:
- via e-mail
Course Objectives
Supporting Knowledge-Intensive Processes
Knowledge-intensive processes are more unstructured processes with a lot of involvements of users with their experience. Supporting such processes at their levels requires modelling and enacting several different forms of knowledge. In general more explicit represented knowledge allows better support. But different forms of knowledge need different intuitive and adequate representations and inferences.
After completion of this module, the participants will be able to assess which kind of knowledge representation and reasoning is adequate and are able to develop appropriate knowledge-based systems. They can value the advantages of knowledge-based systems with respect to their costs. Business Intelligence is concerned with supporting business decisions with facts. It supports business actors in turning data into knowledge that helps to make the right decisions.The module looks at different kinds of decisions (and hence requirements), at different kinds of data and different kinds of tools required to distill knowledge out of data.
Course Contents
- Introduction: Knowledge in processes
- Decision Tables
- Rules
- Textual represented rule (i.e. Horn clauses)
- Forward and backward chaining
- Data-driven and Goal-oriented
- Negation-as-failure
- Object-centred Systems
- F-Logic/Objectlogic
- Fuzzy Logic
- Introduction into Business Intelligence
- Business Performance Management
- Multidimensional modeling
- Data Warehousing
- Data Mining
Study material
Course Material
- Lecture “Introduction”
- Slides: Introduction
- Lecture “Decision Tables”
- Slides: Decision Tables
- Download: Knowledge Work Designer
- Template: Decision Table (Excel)
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- Exercise: Decision Modelling for Admission Process
- Solution: Decision Modelling for Admission Process
- Lecture “Rule-based Systems”
- Slides: Rule-based Systems
- Reasoning example: ancestor
- Exercise: Mini Sudoku and Solution
- Lecture “Forward- and Backward Chaining”
- Slides: Forward- and Backward Chaining
- Lecture “Objectlogic”
- Slides: Objectlogic
- Lecture “Fuzzy logic”
- Slides: Fuzzy Logic
- Exercise: Fuzzy Sets and Solution
- Exercise: Fuzzy Set Operations and Solution
- Exercise: Credit Analysis
- Lecture “Machine Learning”
- Example: Playing Tennis
- Reading Material: Decision Tree Learning
- Lecture “Business Intelligence and Data Warehouse”
- Lecture “Data Use and Data Analysis”
- Slides: Reporting and Dashboards
- Slides: OLAP
- Lecture “Case-based Reasoning”
- Slides: Case-based Reasoning
Course Assignment
The course assignment addresses both main topics of the course and allows you to practice the study material.
- The Data
Exams
Exam Dates A.Y. 2015/2016
- We., 11.05.2016, 14:00 - 15:00, room Kahn
- Th., 16.06.2016, 14:00 - 15:00
- Th., 07.07.2016, 14:00 - 15:00
- Th., 28.07.2016, 14:00 - 15:00
- We., 26.10.2016, 15:00 - 16:00
- Jan/Feb
Exam rules:
you need to pass the
- written exam (counts 70% for the grade)
- course assignment (counts 30% for the grade)