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«Masterstudium Business Informatics an der Technischen Universität Wien Studienplan 2011 in der Fassung vom Juni 2013 Gültig ab 1. Oktober 2013 ...»

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• Creative development of own solutions

Syllabus:

• Lecture: Introduction a historical background; the architecture of a knowledgebased system; problem solving techniques like search (heuristic search, local search), constraint satisfaction problems, planning; formalisms for knowledge representation like description logics, rule-based formalisms; deduction concepts (corresponding to the knowledge representation formalisms) like deduction concepts for classical logic and description logics, recognize-act-cycle (RAC) including the necessary ecient data structures (RETE network); implementations of nonmonotonic behaviour in forward-chaining rule systems, in declarative programming paradigms like, e.g., answer-set programming; development of knowledge-based systems and modern examples.

• Lab: Implementations of concepts, which are important for knowledge-based systems; implementations of knowledge-based systems; for these implementation task, languages from AI (e.g., Lisp) and logic-oriented approaches (e.g., answer-set programming) are used.

Expected Prerequisites: Basic knowledge about propositional and rst-order logic; programming skills; understanding of algorithms; ability to argue formally and to construct simple proofs.

Those topics are taught in the bachelor modules WIN/MOD - Modellierung, STW/MAT

- Mathematik und Theoretische Informatik, INT/ADA - Algorithmen und Datenstrukturen and INT/PRO - Programmkonstruktion.

Teaching and Learning Methods and Adequate Assessment of Performance: The module is organized along lectures, exercises with presentation of the results and discussions, lab assignments to solve larger problems and implement solutions.

Courses of Module:

6.0/4.0 VU KBS for Business Informatics FMF/QOM - Quantative Operations Management ECTS-Credits: 6.0 Summary: This module deals with quantitative methods used in operations management as well as with decision analysis by mathematical models and econometric methods.

Theoretical background is assessed by periodic exams. To adopt the skills, students work out examples and case studies, in class as well as at home.

Learning Outcomes:

Knowledge:

• Choose and formulate appropriate models for various operative decision problems

• Apply causal and time series forecasting

• Parameter estimation and model validation

Skills:

• Students learn to use model-based decision support and adopt an outline of the practical use in the operative division of organisations.

• Students are able to choose among appropriate methods for analytical and forecasting purposes, to work with dierent data sets and problem formulations, and to use the computer to apply discussed methods.

• Students adopt familiarity with elementary econometric methods (linear regression models, time series approaches), specication and testing.

Competences:

• Solving operations management problems, learning to pursue dierent solution strategies, exibility, self-organisation, personal initiative and responsibility, capacity for teamwork, scientic interest

Syllabus:

• Introduction into econometric methods (simple and multiple linear regression methods, stochastic processes); econometric problems of the least squares method;

forecasting techniques (regression, time series approaches, smoothing methods);

assessment of dierent techniques; aconometric software selected applied problems

• Decision analysis, model-based decision support with focus on mathematical models; modelling process; simulation versus mathematical models, optimisation models; measuring productivity and eciency (Data Envelopment Analysis); waiting line models; network planning and graph theory models; inter-temporal optimisation; modelling languages (GAMS); stochastic optimisation Expected Prerequisites: Basic knowledge in analysis, linear algebra, probability theory and statistics. Applying dierential calculus, convexity, Taylor expansion, partial derivatives, matrix algebra, working with random variables (normal, Poisson, exponential distributed) The contents of the bachelor modules STW/MAT - Mathematik und Theoretische Informatik and STW/STA - Statistik und Wahrscheinlichkeitstheorie.

Teaching and Learning Methods and Adequate Assessment of Performance: The module is organized along lectures, illustration by means of examples of use, examples and case studies students work on, in classroom as well as at home.

Courses of Module:

3.0/2.0 VU Econometrics for Business Informatics 3.0/2.0 VU Model-based Decision Support Fachübergreifende Qualikationen ECTS-Credits: 4.5 Summary: Die Lehrveranstaltungen dieses Moduls dienen dem Erwerb fachübergreifender Qualikationen wie zum Beispiel: Verhandlungsführung, Präsentation- und Kommunikationstechnik, systematische Recherche und Planung, Koniktmanagement, Teamfähigkeit und Führung, Organisation und Management, Betriebsgründung und Finanzierung, Verständnis rechtlicher Rahmenbedinungen, Verbesserung von Fremdsprachenkenntnissen.

Learning Outcomes: Durch dieses Modul sollen Studierende Qualikationen erwerben, die über die für das Studium typischen fachlichen Kenntnisse und Fertigkeiten hinausgehen und im Berufsalltag eine wesentliche Rolle spielen, wie zum Beispiel: Verhandlungsführung, Präsentation- und Kommunikationstechnik, systematische Recherche und Planung, Koniktmanagement, Teamfähigkeit und Führung, Organisation und Management, Betriebsgründung und Finanzierung, Verständnis rechtlicher Rahmenbedinungen, Verbesserung von Fremdsprachenkenntnissen.





Courses of Module: Die Lehrveranstaltungen dieses Moduls sind im Umfang von mindestens 9.0 Ects aus dem von der Technischen Universität Wien verlautbarten Katalog von Lehrveranstaltung zum Erwerb von fachübergreifenden Qualikationen sowie aus den folgenden Lehrveranstaltungen zu wählen. Dabei wird empfohlen, Gender-spezische Lehrveranstaltungen zu besuchen.

1.5/1.0 SE Kommunikationstechnik 3.0/2.0 SE Didaktik in der Informatik 3.0/2.0 VU Kooperatives Arbeiten 3.0/2.0 VO Theorie und Praxis der Gruppenarbeit 3.0/2.0 VU Forschungsmethoden 3.0/2.0 VU Softskills für TechnikerInnen 3.0/2.0 VU Italienisch für Ingenieure I 3.0/2.0 VU Technical English Communication 3.0/2.0 VU Technical English Presentation 3.0/2.0 VU Technisches Französisch, Hohes Niveau I 3.0/2.0 VU Technisches Russisch I 3.0/2.0 VU Technisches Spanisch I 3.0/2.0 VO Frauen in Naturwissenschaft und Technik 3.0/2.0 VO Zwischen Karriere und Barriere 3.0/2.0 VU Kommunikation und Moderation 3.0/2.0 SE Kommunikation und Rhetorik 1.5/1.0 VO Präsentation, Moderation und Mediation 3.0/2.0 UE Präsentation, Moderation und Mediation 3.0/2.0 VU Präsentations- und Verhandlungstechnik 3.0/2.0 VU Rhetorik, Körpersprache, Argumentationstraining 1.5/1.0 VO EDV-Vertragsrecht 3.0/2.0 SE Rechtsinformationsrecherche im Internet 3.0/2.0 VO Einführung in die Wissenschaftstheorie I 3.0/2.0 VO Einführung in Technik und Gesellschaft 3.0/2.0 SE Folgenabschätzung von Informationstechnologien 3.0/2.0 VU Techniksoziologie und Technikpsychologie 3.0/2.0 SE Gruppendynamik 3.0/2.0 SE Coaching als Führungsinstrument 1 3.0/2.0 SE Coaching als Führungsinstrument 2 3.0/2.0 VU Barrierefreiheit und Universal Design Freie Wahl ECTS-Credits: max. 4.5 Summary: Die Lehrveranstaltungen dieses Moduls dienen der Vertiefung des Faches sowie der Aneignung auÿerfachlicher Kenntnisse, Fähigkeiten und Kompetenzen.

Courses of Module: Die Lehrveranstaltungen dieses Moduls können frei aus dem Angebot an wissenschaftlichen/künstlerischen Lehrveranstaltungen aller anerkannten inund ausländischen Universitäten gewählt werden, sofern sie der Vertiefung des Faches oder der Aneignung auÿerfachlicher Kenntnisse, Fähigkeiten und Kompetenzen dienen.

Der Umfang der frei wählbaren Lehrveranstaltungen ergänzt den Umfang der übrigen im Studium absolvierten Lehrveranstaltungen auf 90 Ects (oder mehr), wobei ihr Anteil daran 4.5 Ects nicht übersteigen darf.

INT/ASE - Advanced Software Engineering ECTS-Credits: 6.0 Summary: This module builds on the foundation of knowledge from the bachelor module to deepen selected software engineering approaches for the development and evolution of advanced software systems. The focus is on technical software engineering approaches, such as component-based software engineering and software process automation, for evolving advanced software systems in distributed engineering teams. The module consists of project work in a small group over the course of a semester, in which a medium-size software engineering project is conducted with the goal of a usable and useful prototype with associated systematic documentation and the use of selected advanced software technologies.

Learning Outcomes:

Knowledge: Aim of the module is acquire in-depth knowledge on designing and building advanced software systems.

• Architecture styles, such as component-based software engineering

• Lifecycle management and documentation

• Automated software engineering

Skills:

• System-oriented exible way of thinking: choosing, developing, and appropriately applying concepts, models, and tools in the context of an (industrial) ASE project

• Profound strategies for uncommon problems in ASE, such as sustainable advanced software design

• Applying techniques for abstraction and modeling

Competences:

• Problem solving and development in a distributed team environment

• Self-Organization and personal responsibilities

• Managerial and leadership skills in ASE projects

• Collaborative knowledge ownership and management in a medium-sized team

• Proactivity and curiosity on innovative and creative concepts and solution approaches

• Exploration of personal abilities and limits

• Experience in principal-agent relationships, including convincing presentations

Syllabus:

• Software engineering approaches for advanced software systems: Architecture styles, such as component-based software engineering; lifecycle management and documentation; user interface engineering

• Component-based engineering of complex software systems

• Automation in developing advanced software systems: Source code management;

continuous integration and testing; advanced build management; persistence techniques (e.g., object-relational mapping)

• Aspects of enterprize architectures

• Overview on selected research and industrial topics in ASE, including open source software engineering, software engineering for mobile devices, migration project case studies, ecosystems for software engineering tools and frameworks

Expected Prerequisites:

The contents of the bachelor modules INT/PRO - Programmkonstruktion and INT/SEP

- Software Engineering und Projektmanagement.

Teaching and Learning Methods and Adequate Assessment of Performance: The module is organized along a workshop, in which a medium-size software engineering project is conducted with the goal of a usable and useful prototype with associated systematic documentation and the use of selected advanced software technologies, weekly meetings with a tutor, periodic presentations by students on challenges, solution approaches, project status, and project results.

Courses of Module:

6.0/4.0 PR Advanced Software Engineering INT/MEN - Model Engineering ECTS-Credits: 6.0 Summary: This module places software models as the central artifact in software development. Thus, model engineering aims for model-driven software development closing the gap between modeling and programming. In this context, concepts and techniques of transformation engineering, language engineering, and model management are taught.

The module includes lectures and labs. The content of the lecture is deepened in practical exercises, which are solved in small groups.

Learning Outcomes:

Knowledge:

• Broad and integrated knowledge and understanding of scientic foundations of model engineering

• Mastering problem domains, which are characterized by less structured information

• A critical understanding of major theories, principles, and concepts for modeldriven software engineering

• Knowledge and understanding of the current state of literature in this area

Skills:

• Application of knowledge and understanding to solve practical tasks in the eld of model engineering

• Gathering, structuring, evaluating, and interpreting relevant information for particular tasks

Competences:

• Deepening of the own knowledge

• Formulating development-related positions and problem solutions

• Exchanging know-how with computer scientists and domain experts

• Responsibility in a team

• Assessment of the own skills and boundaries

• Dealing with critique

• Self-organization and self-responsibility to independently solve problems

Syllabus:

• Introduction to model engineering: Principles and goals; basic architecture of Model Engineering (ME); notions and denitions; preliminaries and results; approaches; tool support

• Metamodeling: Meta languages; metamodeling languages; metamodeling tools;

language architecture of UML; language extension mechanisms (UML Proles);

domain-specic modeling languages; model serialization and persistence (XMI)

• Object constraint language: Formal specication languages; OCL language constructs; OCL libraries and tool support; OCL as query language and as constraint language; design by contract with OCL

• Model transformation: Model transformation patterns; model transformation languages; graph transformations; transformation standard

• Code generation: Descriptive vs. constructive models; platform specic code generation; template languages; design patterns for code generation

• Dening concrete syntax: Textual modeling language; graphical modeling language



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