Knowledge Engineering and Business Intelligence

Teachers:

• Prof. Dr. Knut Hinkelmann
• Prof. Dr. Holger Wache

Lectures Dates Remaining:

• Schedule

Students Office hours:

• via e-mail

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.

• 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

Course Material

• Organisation

• Lecture “Introduction”
  • Slides: Introduction
  • Davenport, T. H. (2010). Process Management for Knowledge Work. In J. vom Brocke & M. Rosemann (Eds.), Handbook on Business Process Management 1 (pp. 17–36). Berlin, Heidelberg: Springer.

• Lecture “Decision Tables”
  • Slides: Decision Tables
  • Download: Camunda Workflow and Decision Modeler
  • Template: Decision Table (Excel)
  • Exercise: Decision Modeling for the Admission Process
  • Reading: Introduction into DMN

• Lecture “Rule-based Systems”
  • Slides: Rule-based Systems
  • Reasoning example: ancestor
  • Exercise: University and Solution
  • Exercise: Family and Solution
  • Exercise: Further small examples and Solution
  • Exercise: Mini Sudoku and Solution
  • Home Work with Solution
  • Nice browser-based Prolog Engine

• Lecture “Forward- and Backward Chaining”
  • Slides: Forward- and Backward Chaining

• Lecture “Objectlogic”
  • Slides: Objectlogic
  • Exercise: Family and Solution
  • Exercise: ObjectLogic vs Prolog and Solution
  • Homework

• Lecture “Fuzzy logic”
  • Slides: Fuzzy Logic
  • Exercise: Fuzzy Sets and Solution
  • Exercise: Fuzzy Set Operations and Solution
  • Exercise: Credit Analysis

• Lecture “Business Intelligence and Data Warehouse”
  • Slides: Business Intelligence and Data Warehouse

• Lecture “Reporting and Data Analysis”
  • Slides: KPIs and OLAP

• Lecture “Machine Learning: Learning Decision Trees”
  • Slides: Learning Decision Trees
  • Reading Material: Decision Tree Learning
  • Example: Playing Tennis (also included in the lecture slides)
  • Exercise: Auto Traders
  • Exercise: Health Insurance: Combining Learning with Knowledge Engineering - including solution
  • Tool: WEKA Learning Environment
  • Data Sets: playing tennis, creditworthyness (CSV Files), car sales and Health Insurance (ARFF and Excel file)

• Lecture “Case-Based Reasoning”
  • Slides: Case-Based Reasoning

• Lecture “Neural Networks”
  • Slides: Neural Networks

Exam Dates A.Y. 2017/2018

• 23rd of May 2018, 10:00
• 20th of June 2018, 11:00
• 11th of July 2018, 10:00
• 26th of September 2018, 11:00

Exam rules: