1. T-61.6060 Special Course in Computer and Information Science VI P: Decision support with data analysis (5 cr) Introduction lecture Miki Sirola

2. Introduction lecture Seminar cource organization Requirements for passing the cource Instructions for preparing a presentation and a report Introduction to the topic Industrial project about the same topic

3. Course objectives To familiarize the students into this combination of topics To study interdisciplinary approach To practice scientific writing and oral presentation To practise scientific work

4. Course material A collection of scientific articles The students are encouraged to collect more relevant articles For further reading: Baier D., Decker R., Schmidt-Thieme L. (Eds.) Data Analysis and Decision Support

5. Passing the course Oral presentation Written report Opponent in another students presentation 80% participation in the seminar sessions

6. Grading Most important parts: oral presentation and written report Other: acting as an opponent Active participation in the seminar sessions Students own source material (articles)

7. Oral presentation From 20 to 30 minutes plus discussion Windows computer in the room (PowerPoint, PDF) Focus on essential and important issues Clear slides (big enough font, not too much text on each slide, etc.) Clear voice, explaining character, space in front of the screen, etc. basic things in presentation

8. Written report From 5 to 10 pages depending on the topic Emphasize essential things Include also discussion Recommended strucure: abstract, introduction (problem formulation and objectives), main things (self outlined), summary (or conclusion), references. Stucture similar to scientific papers Clear and thorough and exact writing Easy-to-understand style in writing (if difficult terms are needed, they should be explained)

9. Acting as an opponent Opponent reads the material beforehand Opponent prepares some essential questions about the topic Opponent takes actively part in the discussion Other students are encouraged to ask questions as well

10. Writing a scientific paper What problem  Introduction How delt with  Methods What was found  Results What do the findings mean  Discussion Observations Repeatable experiments Critical intellectual process

11. Writing a scientific paper (cont.) Discussion: principles, relationships, generalizations from the results deficiencies relation to previous work (literature), results and interpretation, support or do not support (hypothesis) theoretical implications, practical applications

12. Decision support and data analysis How can we help decision support with data analysis methodologies? Interdisciplinary approach favoured if only possible (depending on the topic)

13. Industrial project Failure management with data analysis Short introduction about this industrial project of related topic

14. Topics Selected application areas Methodological approach Task oriented approach Technology oriented approach Other well-justified topic

15. Possible application areas Process industry Nuclear industry / Power plants Chemical industry Military industry Aviation Medicine Economy Etc.

16. Methodologies Intelligent agents Rule-based (knowledge-based) approach Fuzzy sets Cognitive sciences Etc.

17. Tasks Diagnostics (Failure) prediction Identification and control Visualization Etc.

18. Technologies Internet Multimedia Etc.

19. Example articles Process-Data-Warehousing-Based Operator Support System for Complex Production Technologies Design and Evaluation of an Intelligent Decision Support System for Nuclear Emergencies Decision Support System for Major Accident Prevention in the Chemical Process Industry: a Developers Survey

20. Example articles (cont.) Early Detection and Identification of Dangerous States in Chemical Plants Using Neural Networks Expert System for Aircraft Maintenance Service Industry Model Selection for Medical Diagnostic Decision Support System: a Breast Cancer Detection Case

21. Example articles (cont.) A Multilayer Perception-Based Medical Decision Support System for Heart Disease Diagnosis Integrated Web-Based Architecture for Correlative Engineering Data Analysis and Decision Support Business Rule Based Data Analysis for Decision Support and Automation

22. Questions