Universität Wien

040189 KU Knowledge Management (MA) (2022W)

8.00 ECTS (4.00 SWS), SPL 4 - Wirtschaftswissenschaften
Continuous assessment of course work
ON-SITE


Zur endgültigen Lehrveranstaltung-Aufnahme ist ein pünktliches Erscheinen zur Vorbesprechung notwendig. Unentschuldigtes Fernbleiben führt zum Verlust des Lehrveranstaltungsplatzes.

Registration/Deregistration

Note: The time of your registration within the registration period has no effect on the allocation of places (no first come, first served).

Details

max. 30 participants
Language: German

Lecturers

Classes (iCal) - next class is marked with N

concerning the 2 tests:
1. Test: Wed. 23.11.2022; 08:00-09:30; Hörsaal 6 OMP
2. Test: Wed. 11.01.2022; 08:00-09:30; Hörsaal 6 OMP

Wednesday 05.10. 08:00 - 09:30 Hörsaal 7 Oskar-Morgenstern-Platz 1 1.Stock
Wednesday 05.10. 09:45 - 11:15 Hörsaal 17 Oskar-Morgenstern-Platz 1 2.Stock
Wednesday 12.10. 08:00 - 09:30 Hörsaal 7 Oskar-Morgenstern-Platz 1 1.Stock
Wednesday 12.10. 09:45 - 11:15 Hörsaal 17 Oskar-Morgenstern-Platz 1 2.Stock
Wednesday 19.10. 08:00 - 09:30 Hörsaal 7 Oskar-Morgenstern-Platz 1 1.Stock
Wednesday 19.10. 09:45 - 11:15 Hörsaal 17 Oskar-Morgenstern-Platz 1 2.Stock
Wednesday 09.11. 08:00 - 09:30 Hörsaal 7 Oskar-Morgenstern-Platz 1 1.Stock
Wednesday 09.11. 09:45 - 11:15 Hörsaal 17 Oskar-Morgenstern-Platz 1 2.Stock
Thursday 10.11. 11:30 - 14:45 Seminarraum 1 Oskar-Morgenstern-Platz 1 Erdgeschoß
Friday 11.11. 09:45 - 11:15 Seminarraum 4 Oskar-Morgenstern-Platz 1 1.Stock
Friday 11.11. 11:30 - 14:45 Seminarraum 4 Oskar-Morgenstern-Platz 1 1.Stock
Wednesday 16.11. 08:00 - 09:30 Hörsaal 7 Oskar-Morgenstern-Platz 1 1.Stock
Wednesday 16.11. 09:45 - 11:15 Hörsaal 17 Oskar-Morgenstern-Platz 1 2.Stock
Wednesday 23.11. 08:00 - 09:30 Hörsaal 7 Oskar-Morgenstern-Platz 1 1.Stock
Monday 28.11. 09:45 - 11:15 Seminarraum 13 Oskar-Morgenstern-Platz 1 2.Stock
Wednesday 30.11. 08:00 - 09:30 Hörsaal 7 Oskar-Morgenstern-Platz 1 1.Stock
Wednesday 07.12. 08:00 - 09:30 Hörsaal 7 Oskar-Morgenstern-Platz 1 1.Stock
Wednesday 14.12. 08:00 - 09:30 Hörsaal 7 Oskar-Morgenstern-Platz 1 1.Stock
Wednesday 11.01. 08:00 - 09:30 Hörsaal 7 Oskar-Morgenstern-Platz 1 1.Stock
Wednesday 18.01. 08:00 - 09:30 Hörsaal 7 Oskar-Morgenstern-Platz 1 1.Stock
Tuesday 24.01. 15:00 - 20:00 Hörsaal 16 Oskar-Morgenstern-Platz 1 2.Stock
Wednesday 25.01. 08:00 - 09:30 Hörsaal 7 Oskar-Morgenstern-Platz 1 1.Stock
Wednesday 25.01. 09:45 - 11:15 Hörsaal 12 Oskar-Morgenstern-Platz 1 2.Stock
Wednesday 25.01. 11:30 - 16:30 Hörsaal 7 Oskar-Morgenstern-Platz 1 1.Stock
Wednesday 25.01. 16:45 - 20:00 Hörsaal 12 Oskar-Morgenstern-Platz 1 2.Stock
Thursday 26.01. 08:00 - 11:15 Hörsaal 17 Oskar-Morgenstern-Platz 1 2.Stock
Thursday 26.01. 11:30 - 14:45 Hörsaal 16 Oskar-Morgenstern-Platz 1 2.Stock
Thursday 26.01. 15:00 - 16:30 Hörsaal 7 Oskar-Morgenstern-Platz 1 1.Stock
Thursday 26.01. 16:45 - 20:00 Hörsaal 17 Oskar-Morgenstern-Platz 1 2.Stock

Information

Aims, contents and method of the course

The course is divided into two parts:

Part 1: Course Knowledge Management (2 hr)
The contents of the course are: Knowledge management, knowledge representation, knowledge engineering, especially logic-based systems, fuzzy logic, neural networks, expert systems, ontologies, modeling and metamodeling Introduction to knowledge management, comparison of tools and software products. The areas of (a) classical knowledge processing, (b) fuzzy logic and fuzzy systems, and (c) artificial neural networks; are presented at three levels (modeling level, development and implementation level, deployment level).

This part of the course is divided into a theoretical and a practical part.
The theoretical part explains the basics of knowledge processing in a frontal teaching with compulsory attendance and cooperation. The content can be taken either from the course book, on div. Internet pages as well as from the frontal teaching. The performance monitoring consists of two tests.
The practical part consists of a group project in which a digital service is first (a) described, (b) then modeled, and (c) realized towards the end of the semester. The practical part is carried out in the form of workshops, tutorials and independent work time. The performance assessment is the final presentation as well as the interim submissions (a,b,c).

This part of the course is taught and examined in German. The practical part can also be conducted in English.

Part 2: Literature seminar Knowledge Management (2 hr)
The seminar paper is a scientific paper with the following evaluation criteria: (1) Positioning of the topic; Why is this topic interesting? (2) Method of content preparation; How was the content prepared? (3) Methodical approach; What is described? (4) Compliance with formal criteria; Are format templates, citation guidelines and references executed correctly and transparently?
Seminar papers are preparatory work for the diploma thesis and are intended to practice the scientific preparation of content in addition to the development and deepening of relevant topics of study.
The literature work is open-topic in the areas of data management, business intelligence, industry 4.0, business process management and knowledge management, with the option to choose from given topics.

The literature seminar will be conducted in the form of individual work, with fixed group appointments and individual interaction offered for supervision. The performance control takes place after plausibility of the own work and the plagiarism control exclusively at the delivered document on the basis of (1) positioning of the topic, (2) methodical procedure, (3) presentation of the contents and (4) the formal criteria.

The paper has to be written in English (or German), the supervision can take place in German and English.

Assessment and permitted materials

Part 1: Course Knowledge Management (2 hr)
(1) Active participation in the course by means of two tests (no papers allowed): 40%
(2) Assessment of the practical project based on the intermediate submissions and the final presentation (documents allowed): 60%

Part 2: Literature seminar Knowledge Management (2 hr)
(3) Plausibility check whether the text has been prepared by the student. The final work is subjected to a plagiarism check by means of submission via the eLearning platform Moodle (Turn-It-In). In case of plagiarism or preparation of the work by a third person, the work will be reported as cheating and the achievement will be judged as having been obtained by fraud.
(4) Positioning of the text (15%), content preparation (35%), methodical approach (25%), formal criteria (25%)

For positive completion of the course, both parts must be positively passed independently of each other.

Minimum requirements and assessment criteria

Part 1: Course Knowledge Management (2 hr) (50% of the total assessment)
• Compulsory attendance with max. 3 excused absences.
• Total sum of test and practical project must be > 50%.
Theory part: 1. Intermediate test 20%, 2. Intermediate test 20% = 40%, collaboration can change the final grade.
Practical project: The realization of the project is a prerequisite for an assessment, 1. realization of the project 40%, 2. modeling 20%, 3. description of the knowledge product is to be understood as a topic assignment. (all partial project performances must have been submitted in due time)

Part 2: Literature seminar Knowledge Management (2 hr) (50% of the total assessment)
• No attendance required during supervision, attendance required during topic assignment and self-paced review.
• Assessment of the literature work must be > 50%.

For positive completion of the course, both parts must be positively passed independently of each other.
The total sum gives the grade: 50%<62,5%: 4; 62,5%<75%: 3; 75%<87,5%: 2; 87,5%<=100%: 1

Examination topics

The examination material will be announced during the course.

Reading list

The basis literature is Karagiannis, Dimitris / Telesko, Rainer: Knowledge Management Concepts of Artificial Intelligence and Soft Computing.
http://austria.omilab.org/psm/omirob
Additional literature will be announced during the course.

Association in the course directory

Last modified: Su 20.11.2022 20:07