Knowledge Engineering and Business Intelligence
News
Dear students,
We will have an additional lecture on Wednesday, 3rd of June, 12:45pm - 2:00pm. The link is https://fhnw.zoom.us/j/95153387074
We will give a short repetition of the topics and how they relate to each other. Furthermore, there will be a Q&A session.
Best regards, Knut
General Info
Teachers:
- Knut Hinkelmann
- Holger Wache
Schedule:
ESSE3 Link
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
- Human-interpretation vs. machine-interpretation
- Graphical modeling
- Ontology-based modeling
- Machine Learning: Learning Decision Trees
- Case-Based Reasoning
- Neuronal Networks
- Business Intelligence
- Business Performance Management
- Data Warehouse
- Reporting and Data Analysis
Study material
Course Material
- Lecture “Introduction”
- Slides: Introduction
- Recording (Password on request)
- Homework: Types of Knowledge
- Lecture “Knowledge in Processes”
- Example: Decision-Aware Business Process
- Lecture “Decision Tables”
- Slides: Decision Tables - DMN
- Reading: Introduction into DMN
- Exercise: Reduction of Decision Tables, Sample Table
- Exercise and Solution: Decision Modelling for Admission, DMN file of the decision table
- Download: Camunda Workflow and Decision Modeler
- Recording (Password on request)
- Lecture “Rule-based Systems”
- Whiteboard: Holger's Whiteboard
- Slides: Rule-based Systems
- Reasoning example: ancestor
- Exercise: University and Solution
- Exercise: Further small examples and Solution
- Exercise: Mini Sudoku and Solution
- Exercise: Friendship and Solution
- Nice browser-based Prolog Engine
- Recording first day (Password on request)
- Recording second day (Password on request)
- Lecture “Forward- and Backward Chaining”
- Slides: Forward- and Backward Chaining
- Lecture “Knowledge Nets and RDF”
- Whiteboard: Holger's Whiteboard
- Slides: Knowledge Nets and RDF
- Exercise: RDF Graphs and Solution
- Exercise: RDF Schema and Solution
- Exercise: RDF Schema Inferences and Solution
- Recording 1 (Password on request)
- Recording 2 (Password on request)
- Lecture “Fuzzy logic”
- Whiteboard: Holger's Whiteboard
- Slides: Fuzzy Logic
- Exercise: Fuzzy Sets and Solution
- Exercise: Fuzzy Set Operations and Solution
- Exercise: Credit Analysis
- Recording (Password on request)
- Lecture “Ontology Engineering”
- Slides: Ontology Engineering
- Exercise: Business Process Ontology
- Literature: Noy, N. F., & McGuinness, D. L. (2001). Ontology development 101: A guide to creating your first ontology. Stanford Knowledge Systems Laboratory Technical Report KSL-01-05.
- Recording (Password on request)
- Lecture “Modelling and Metamodelling”
- Slides: Modelling and Metamodelling
- Download: ADOxx Model Engineering Environment
- Recording (Password on request)
- Lecture “Ontology-based Metamodelling”
- Slides: Ontology-based Metamodelling
- Practice Session: Agile Ontology-based Modelling for Design
- Recording (Password on request)
- Lecture “Symbolic Machine Learning”
- Slides: The Idea of Machine Learning
- Slides: Learning Rules
- Reading Material: Decision Tree Learning
- Exercise: Auto Traders
- Recording (Password on request)
- Lecture “Combining Machine Learning and Knowledge Engineering”
- Example: Machine Learning and Knowledge
- Recording (password on request)
- Lecture “Neuronal Networks”
- Whiteboard: Holger's Whiteboard
- Slides: Neuronal Networks
- Example for Backpropagation: Excel
- Colab example: Fraud detection of credit card usage
- Recording (Password on request)
- Lecture “Case-Based Reasoning”
- Slides: Case-Based Reasoning
- Assignment and Solution: CBR for Health Insurance Applications
- Recording (Password on request)
- Lecture “Business Intelligence”
- Lecture: “Business Performance Management”
- Exercise: Balanced Scorecard for Swiss Bikes
- Case: Swiss Bikes
- Lecture: “Data Warehousing”
- Slides: Data Warehousing
- Recording BI, Peformance Management and Data Warehouse (Password on request)
- Lecture: “Reporting and Online Analytic Processing”
- Poster: Better Reports
- Recording (Password on request)
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- Recording (Password on request)
Exams
Exam Dates A.Y. 2019/2020
- 15th of June 2020
- 30th of June 2020
- 29th of July 2020
- 5th Of October 2020
- 2nd of November 2020
Exam rules:
