====== Knowledge Engineering and Business Intelligence ======
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===== News =====
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**Lectures Dates Remaining**:
  * Tuesday, 14th of May, 10 - 13 (AB2)
  * Wedndesday, 15th of May, 10 - 13 (AB1)
  * Monday, 3rd of June, 14 - 18 (AB2)
  * Tuesday, 4th of June, 10 - 13 (AB2)
  * Wedndesday, 5th of June, 9 - 13 (AB1)
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===== General Info =====
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**Teachers**: 
  * Knut Hinkelmann
  * Holger Wache

**ESSE3 Link**
  * [[https://didattica.unicam.it/Guide/PaginaADErogata.do?ad_er_id=2018*N0*N0*S2*12329*8709&ANNO_ACCADEMICO=2018&mostra_percorsi=S|Knowledge Engineering and Business Intelligence - AY 2018/2019]]

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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
  * Business Intelligence & Data Warehouse
  * Reporting and Data Analysis
  * Machine Learning: Learning Decision Trees
  * Case-Based Reasoning
  * Neuronal Networks
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{{ :didattica:magistrale:kebi:ay_1819:ke-3-logic_programming.pdf |}}
===== Study material =====

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**Course Material**
  * {{ :didattica:magistrale:kebi:ay_1819:ke-0-organization.pdf |Organisation}}

  * Lecture "Introduction"
    * Slides: {{ :didattica:magistrale:kebi:ay_1819:ke-1-introduction_knowledge_in_processes.pdf |Introduction}} 
    * {{ :didattica:magistrale:kebi:ay_1819:davenport_2010_process_management_for_knowledge_work.pdf |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: {{ :didattica:magistrale:kebi:ay_1819:ke-2-decisontables.pdf |Decision Tables}}
    * Download: [[https://camunda.org/|Camunda Workflow and Decision Modeler]]
    * Template: {{ :didattica:magistrale:kebi:ay_1819:dmn_decision_table_template.xlsx |Decision Table (Excel)}}
    * Exercise: {{ :didattica:magistrale:kebi:ay_1819:exercise_decision_modeling_admission.pdf |Decision Modeling for the Admission Process}}
    * Reading: [[http://blog.maxconsilium.com/2014/09/introduction-to-decision-model-notation.html|Introduction into DMN]]

  * Lecture “Rule-based Systems”
    * Slides: {{ :didattica:magistrale:kebi:ay_1819:ke-3-logic_programming.pdf |Rule-based Systems}}
    * Reasoning example: {{ :didattica:magistrale:kebi:ay_1718:ke-3-logic_programming-reasoningexample_ancestor.pdf |ancestor}}
    * Exercise: {{ :didattica:magistrale:kebi:ay_1819:ke-3-1-exercise_university.pdf |University}} and {{ :didattica:magistrale:kebi:ay_1819:ke-3-1-exercise_university_solution.pdf |Solution}}
    * Exercise: {{ :didattica:magistrale:kebi:ay_1819:ke-3-2-exercise_family_rules.pdf |Family}} and {{ :didattica:magistrale:kebi:ay_1819:ke-3-2-exercise_family_rules_solution.pdf |Solution}}
    * Exercise: {{ :didattica:magistrale:kebi:ay_1819:ke-3-3-exercise_smallexamples.pdf |Further small examples}} and {{ :didattica:magistrale:kebi:ay_1819:ke-3-3-exercise_smallexamples_solution.pdf |Solution}}
    * Exercise: {{ :didattica:magistrale:kebi:ay_1819:ke-3-5-exercise_minisudoku.pdf |Mini Sudoku}} and {{ :didattica:magistrale:kebi:ay_1819:ke-3-5-exercise_minisudoku_solution.pdf |Solution}}
    * {{ :didattica:magistrale:kebi:ay_1819:ke-3-homeexercise_masterdecisions.pdf |Home Work}} with Solution
    * Nice browser-based [[http://swish.swi-prolog.org|Prolog Engine]]

  * Lecture “Forward- and Backward Chaining”
    * Slides: {{ :didattica:magistrale:kebi:ay_1819:ke-4_fc_vs_bc.pdf |Forward- and Backward Chaining}}

  * Lecture “Objectlogic”
    * Slides: {{ :didattica:magistrale:kebi:ay_1819:ke-5-objectlogic.pdf |Objectlogic}}
    * Exercise: {{ :didattica:magistrale:kebi:ay_1819:ke-5-1-exercise_objectlogic_at.pdf |Family}} and Solution
    * Exercise: {{ :didattica:magistrale:kebi:ay_1819:ke-5-2-exercise_objectlogic.pdf |ObjectLogic vs Prolog}} and Solution
    * Homework

  * Lecture “Fuzzy logic”
    * Slides: {{ :didattica:magistrale:kebi:ay_1819:ke-6-fuzzylogic.pdf |Fuzzy Logic}}
    * Exercise: {{ :didattica:magistrale:kebi:ay_1819:ke-6-1-exercise_define_fuzzy_set.pdf |Fuzzy Sets}} and Solution
    * Exercise: {{ :didattica:magistrale:kebi:ay_1819:ke-6-2-exercise_fuzzy_set_operations.pdf |Fuzzy Set Operations}} and Solution
    * Exercise: {{ :didattica:magistrale:kebi:ay_1819:ke-6-3-credit_analysis.pdf |Credit Analysis}}

  * Lecture "Machine Learning: Learning Decision Trees"
    * Slides: {{ :didattica:magistrale:kebi:ay_1819:ke-7-1_machine_learing_and_knowledge_engineering.pdf |Machine Learning and Knowledge Engineering}}
    * Slides: {{ :didattica:magistrale:kebi:ay_1819:ke-7-2_symbolic_learning_-_learning_decision_trees.pdf |Learning Decision Trees}}
    * Reading Material:  {{ :didattica:magistrale:kebi:ay_1718:decision_tree_learning_lecture.pdf |Decision Tree Learning}}
    * Example: {{ :didattica:magistrale:kebi:ay_1819:ke-7-3_example_id3.pdf |Playing Tennis (also included in the lecture slides)}}
    * Exercise: {{ :didattica:magistrale:kebi:ay_1718:exercise_learning_carsales.pdf |Auto Traders}}
    * Exercise: {{ :didattica:magistrale:kebi:ay_1819:exercise_health_insurance_learning.pdf |Health Insurance: Learning Risk Assessment}}
    * Assignment: {{ :didattica:magistrale:kebi:ay_1819:assignment_health_insurance_knowledge.pdf |Health Insurance: Combining Learning with Knowledge Engineering}}
    * Tool: {{ :didattica:magistrale:kebi:ay_1718:weka_introduction.pdf |WEKA Learning Environment}}
    * Data Sets: {{ :didattica:magistrale:kebi:ay_1718:datasets.zip |playing tennis, creditworthyness (CSV Files), car sales and Health Insurance (ARFF and Excel file)}}

  * Lecture "Neural Networks"
    * Slides: {{ :didattica:magistrale:kebi:ay_1819:ke-8_neural_networks.pdf |Neural Networks}}

  * Lecture "Case-Based Reasoning"
    * Slides: {{ :didattica:magistrale:kebi:ay_1819:ke-9-cbr.pdf |Case-Based Reasoning}}

  * Lecture "Business Intelligence"
    * {{ :didattica:magistrale:kebi:ay_1819:ke-10-business_intelligence_intro.pdf |Business Intelligence: Making Informed Decisions}}

  * Lecture: "Business Performance Management"
    * Slides: {{ :didattica:magistrale:kebi:ay_1819:ke-11_bi_bpm.pdf |Business Performance Management and Balanced Scorecard}}
    * Slides: {{ :didattica:magistrale:kebi:ay_1819:ke-11-2_bi_bsc_with_adoscore.pdf |Balanced Scorecard with ADOscore}}
    * Workshop: Balanced Scorecard for SwissBikes with ADOscore
      * Tasks: {{ :didattica:magistrale:kebi:ay_1819:workshop_bsc_for_swissbikes.pdf |Balanced Scorecard for Swiss Bikes}}
      * Case: {{ :didattica:magistrale:kebi:ay_1819:swissbikes1-04_engl.pdf |Swiss Bikes}}
      * Solution: {{ :didattica:magistrale:kebi:ay_1819:exercise_bsc_swissbikes.pdf |Balanced Scorecard Development for SwissBikes}}
      * Models: {{ :didattica:magistrale:kebi:ay_1819:bsc_swissbikes.zip |Balanced Scorecard Models for import in ADOscore}}
    * Software: [[http://knut.hinkelmann.ch/download/ADOscore_stand-alone.zip|ADOscore Standalone]]
      * {{ :didattica:magistrale:kebi:ay_1819:adoscore-installation.pdf |ADOscore Installation Instruction}} (Licence can be requested from Knut)

  * Lecture: "Data Warehousing"
    * Slides: {{ :didattica:magistrale:kebi:ay_1819:ke-12-bi_dwh.pdf |Data Warehousing}}
    * Book: [[http://www.essai.rnu.tn/Ebook/Informatique/The%20Data%20Warehouse%20Toolkit,%203rd%20Edition.pdf|Kimball, R and Ross, M. (2013). The Data Warehouse Toolkit, 3rd Edition, Wiley and Sons.]]

  * Lecture: "Reporting and Dashboards"
    * Slides: {{ :didattica:magistrale:kebi:ay_1819:ke-13_bi_reporting_statistics.pdf |BI Tools - Frontend: Reporting and Dashboards
}}
    * Poster: {{ :didattica:magistrale:kebi:ay_1819:better_reports_poster_by_ibcs.pdf |Better Reports}}


  * Lecture: "Online Analytic Processing"
    * Slides: {{ :didattica:magistrale:kebi:ay_1819:ke-14_bi_olap.pdf |BI Tools - Frontend: Online Analytic Processing}}



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===== Exams =====
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**Exam Dates A.Y. 2018/2019**
  * 19th of June 2019, 10:00
  * 3rd of July 2019, 10:00
  * 24th of July 2019, 10:00

**Exam rules**: 


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