====== Knowledge Engineering and Business Intelligence ======
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===== News =====
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  * <wrap em>**February 29th, 2016**</wrap>: The course started!!
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===== General Info =====
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**Teachers**: 
  * [[http://knut.hinkelmann.ch/|Prof. Dr. Knut Hinkelmann]] 
  * [[http://web.fhnw.ch/personenseiten/holger.wache/|Prof. Dr. Holger Wache]]

**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
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===== Course Objectives =====

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== 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.
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===== Course Contents =====

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  * 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
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===== Study material =====
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**Course Material**
  * {{:didattica:magistrale:kebi:ay_1516:ke-0-organization.pdf|Organisation}}

  * Lecture "Introduction"
    * Slides: {{:didattica:magistrale:kebi:ay_1516:ke-1-introduction_knowledge_in_processes.pdf|Introduction}}

  * Lecture "Decision Tables"
    * Slides: {{:didattica:magistrale:kebi:ay_1516:ke-2-decisontables.pdf|Decision Tables}} 
    * Download: [[http://knut.hinkelmann.ch/download/kwd.zip|Knowledge Work Designer]]
    * Template: {{:didattica:magistrale:kebi:ay_1516:dmn_decision_table_template.xlsx|Decision Table (Excel)}}
    * Exercise: {{:didattica:magistrale:kebi:ay_1516:exercise_distinguish_process_and_business_logic.pdf|Process Logic vs. Business Logic for Insurance Application}}
      * {{:didattica:magistrale:kebi:ay_1516:models.zip|Models for Insurance Application (Excel and ADL file for import in Knowledge Work Designer)}}
    * Exercise: {{:didattica:magistrale:kebi:ay_1516:exercise_decision_modeling_admission.pdf|Decision Modelling for Admission Process}}
      * Solution: {{:didattica:magistrale:kebi:ay_1516:exercise_decision_modeling_admission_solution.pdf|Decision Modelling for Admission Process}}
      * {{:didattica:magistrale:kebi:ay_1516:decision_tables_from_lecture_for_eligibilty_and_admission.xlsx|Decision tables from lecture for Eligibilty and Admission}}
      * {{:didattica:magistrale:kebi:ay_1516:admission_models.zip|Admission Process and Decision Model for import in Knoweldge Work Designer}}

  * Lecture "Rule-based Systems"
    * Slides: {{:didattica:magistrale:kebi:ay_1516:ke-3-logic_programming.pdf|Rule-based Systems}}
    * Exercise: {{:didattica:magistrale:kebi:ay_1516:ke-3-1-exercise_family_rules.pdf|Family}} and {{:didattica:magistrale:kebi:ay_1516:ke-3-1-exercise_family_rules_solution.pdf|Solution}}
    * {{:didattica:magistrale:kebi:ay_1516:ke-3-homeexercise_masterdecisions.pdf|Home Work}} with {{:didattica:magistrale:kebi:ay_1516:ke-3-homeexercise_masterdecisions_solution.pdf|solution}}
    * Reasoning example: {{:didattica:magistrale:kebi:ay_1516:ke-3-logic_programming-reasoningexample_ancestor.pdf|ancestor}}
    * Exercise: {{:didattica:magistrale:kebi:ay_1516:ke-3-2-exercise_minisudoku.pdf|Mini Sudoku}} and {{:didattica:magistrale:kebi:ay_1516:ke-3-2-exercise_minisudoku_solution.pdf|Solution}}
    * Nice [[http://swish.swi-prolog.org|browser-based Prolog Engine]]

  * Lecture "Forward- and Backward Chaining"
    * Slides: {{:didattica:magistrale:kebi:ay_1516:ke-4_fc_vs_bc.pdf|Forward- and Backward Chaining}}

  * Lecture "Objectlogic"
    * Slides: {{:didattica:magistrale:kebi:ay_1516:ke-5-objectlogic.pdf|Objectlogic}}
    * Exercise: {{:didattica:magistrale:kebi:ay_1516:ke-5-1-exercise_objectlogic_at.pdf|Family}} and {{:didattica:magistrale:kebi:ay_1516:ke-5-1-exercise_objectlogic_at_solution.pdf|Solution}}
    * {{:didattica:magistrale:kebi:ay_1516:ke-5-homeexercise_masterdecisions.pdf|Homework}}

  * Lecture "Fuzzy logic"
    * Slides: {{:didattica:magistrale:kebi:ay_1516:ke-6-fuzzylogic.pdf|Fuzzy Logic}}
    * Exercise: {{:didattica:magistrale:kebi:ay_1516:ke-6-1-exercise_define_fuzzy_set.pdf|Fuzzy Sets}} and {{:didattica:magistrale:kebi:ay_1516:ke-6-1-exercise_define_fuzzy_set_solution.pdf|Solution}}
    * Exercise: {{:didattica:magistrale:kebi:ay_1516:ke-6-2-exercise_fuzzy_set_operations.pdf|Fuzzy Set Operations}} and {{:didattica:magistrale:kebi:ay_1516:ke-6-2-exercise_fuzzy_set_operations_solution.pdf|Solution}}
    * Exercise: {{:didattica:magistrale:kebi:ay_1516:ke-6-3-credit_analysis.pdf|Credit Analysis}}

  * Lecture "Machine Learning"
    * Slides: {{:didattica:magistrale:kebi:ay_1516:ke-7_learning_decision_trees.pdf|Machine Learning - Learning Decision Trees}}
    * Example: {{:didattica:magistrale:kebi:ay_1516:ke-7_example_id3.pdf|Playing Tennis}}
    * Data sets:{{:didattica:magistrale:kebi:ay_1516:datasets.zip|playing tennis and creditworthyness (CSV Files)}}
    * {{:didattica:magistrale:kebi:ay_1516:assignment_learning_carsales.pdf|Assignment}}
    * Reading Material: {{:didattica:magistrale:kebi:ay_1516:ke-7-paper_decisiontree.pdf|Decision Tree Learning}} 

  * Lecture "Business Intelligence and Data Warehouse"
    * Slides: {{:didattica:magistrale:kebi:ay_1516:ke-8-business_intelligence_dwh.pdf|Business Intelligence and Data Warehouse}}

  * Lecture "Data Use and Data Analysis"
    * Slides: {{:didattica:magistrale:kebi:ay_1516:ke-9-bi_reporting.pdf|Reporting and Dashboards}}
    * Slides: {{:didattica:magistrale:kebi:ay_1516:ke-10-bi_olap.pdf|OLAP}}

  * Lecture "Case-based Reasoning"
    * Slides: {{:didattica:magistrale:kebi:ay_1516:ke-11-cbr.pdf|Case-based Reasoning}}

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===== Course Assignment =====
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The course assignment addresses both main topics of the course and allows you to practice the study material.
  * The {{:didattica:magistrale:kebi:ay_1516:assignment.pdf|Course Assignemnt}}
  * The {{:didattica:magistrale:kebi:ay_1516:assignment_learning.arff.zip|Data}}
  * {{:didattica:magistrale:kebi:ay_1516:weka_introduction.pdf|WEKA Introduction}}
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===== Exams =====
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**Exam Dates A.Y. 2015/2016**
  * We., 11.05.2016, 14:00 - 15:00,  <wrap em>**room Kahn**</wrap>
  * 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)

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