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Teaching

Applications of Parallel and Distributed Systems

Teaching activities in the department

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Courses offered by the department cover a broad spectrum of topics on capturing, managing and analysing huge datasets. In the following, we list the courses currently offered. More information regarding the content of the courses is available in the module handbook.

Current Courses of the Department (as listed in Campus)

Data Management for End-to-End Machine Learning

Hauptseminar
2 SWS
SS 22

Semester:	SS 22
Content:	The comprehensive analysis of large volumes of data is crucial in modern information systems. Some of the main driving forces are current trends such as Industrie 4.0 and the Internet of Things (IoT). In particular, applying machine learning (ML) to data of real-world scenarios imposes various data management challenges in several steps of an end-to-end ML application. Very often, the characteristics of real-world data impose challenges themselves. For instance, data may change over time (data set shifts in non-stationary environments). Here, major problems are how to detect and characterize these changes (e.g., different concept drifts), as well as how to consider them in adaptive machine learning strategies. Furthermore, organizations typically train a multitude of machine learning models from their data. These ML models and their lifecycle have to be managed in a comprehensive way. Afterwards, the most suitable models have to be selected for a concrete application, e.g., via approaches to model discovery or AutoML. Moreover, many machine learning models provide some kind of prediction. Here, it is mandatory for the users to understand why a certain prediction was provided by the model. Finding methods to offer such explanations and thus to help people interpret the models correctly, instead of treating them just as black boxes, is another important challenge. In this seminar, we address current technologies, concepts, algorithms and system infrastructures for above-mentioned challenges for data management in end-to-end machine learning, for example: • Machine learning with complex data characteristics, e.g., in non-stationary environments • Comprehensive management of machine learning models • Automatic selection of machine learning models (e.g., AutoML, Meta-Learning) • Explainable artificial intelligence / interpretable machine learning
Number:	021195000
Type:	Hauptseminar
Semester hours:	2 SWS
Link:	C@MPUS
Organisation:	Datenbanken und Informationssysteme

GSaME-Kolloquium

Kolloquium
2 SWS
SS 22

Semester:	SS 22
Content:	nur für Mitarbeiterinnen und Doktorandinnen
Number:	021198100
Type:	Kolloquium
Semester hours:	2 SWS
Link:	C@MPUS
Organisation:	Datenbanken und Informationssysteme

Kolloquium Datenbanksysteme

Kolloquium
2 SWS
SS 22

Semester:	SS 22
Content:	nur für Mitarbeiterinnen und Doktorandinnen
Number:	021198000
Type:	Kolloquium
Semester hours:	2 SWS
Link:	C@MPUS
Organisation:	Datenbanken und Informationssysteme

Speicherung, Verarbeitung und Analyse von Zeitreihendaten

Seminar
2 SWS
SS 22

Semester:	SS 22
Content:	https://www.f05.uni-stuttgart.de/informatik/dokumente/Seminare/Seminare_22S/10_Mitschang-MA.pdf
Number:	021185000
Type:	Seminar
Semester hours:	2 SWS
Link:	C@MPUS
Organisation:	Datenbanken und Informationssysteme

Data Science mit Python

Vorlesung
1 SWS
SS 22

Semester:	SS 22
Content:	Datenakquise Datenbereinigung und –transformation Datenmodellierung Datenmanagement Datenverarbeitung Visualisierung von Analyseergebnissen
Number:	021100100
Type:	Vorlesung
Semester hours:	1 SWS
Link:	C@MPUS
Organisation:	Datenbanken und Informationssysteme

Industrial Analytics

Vorlesung
1 SWS
SS 22

Semester:	SS 22
Content:	In der Veranstaltung werden insbesondere folgende Themen bearbeitet: - Einführung Industrie 4.0 und Industrial Analytics inkl. konkreten Anwendungsbeispielen aus der industriellen Praxis - Grundlagen industrieller Wertschöpfung, speziell industrielle Prozesse und Produktlebenszyklus - Industrielle IT-Systeme und industrielle Daten, z.B. MES, PLM- und ERP-Systeme - Datenplattformen für Industrial Analytics, insbesondere Data Lakes und Datenkataloge mit zugehörigen Architekturen und Technologien - Data-Analytics-Anwendungen, von Reporting bis Machine Learning - Data Governance und Datenstrategien im industriellen Umfeld - Praktische Übungen zu den o.g. Themenbereichen
Number:	021100300
Type:	Vorlesung
Semester hours:	1 SWS
Link:	C@MPUS
Organisation:	Datenbanken und Informationssysteme

Advanced Information Management

Vorlesung
2 SWS
SS 22

Semester:	SS 22
Content:	Die Vorlesung behandelt aktuelle Modellierungs-, Entwicklungs- und Verarbeitungskonzepte DB-basierter Anwendungssysteme. Hierzu gehört insbesondere die Verarbeitung strukturierter, semi-strukturierter und unstrukturierter Daten sowie Techniken zur Integration von Informationen aus heterogenen Datenquellen. Unter anderem werden folgende Themenbereiche vertieft: - XML und Datenbanktechnologie (XML-Modellierung, XML-Speicherung, XML-Anfragesprachen, - XML-Verarbeitung) - Content Management (Enterprise Content Management, Information Retrieval, Suchtechnologien) - NoSQL Datenmanagement (Key value stores, MapReduce, triple stores, document stores, graph stores)
Number:	021151000
Type:	Vorlesung
Semester hours:	2 SWS
Link:	C@MPUS
Organisation:	Datenbanken und Informationssysteme

Modellierung

Vorlesung
2 SWS
SS 22

Semester:	SS 22
Content:	Die Vorlesung vermittelt wie wesentliche Artefakte eines IT Systems modelliert werden, den Zusammenhang und das Zusammenspiel solcher Artefakte sowie die Rolle von Metamodellen und deren Erstellung. Die Vorlesung beinhaltet folgende Themen: - Entity-Relationship Modell und komplexe Objekte - Relationenmodell und Relationenalgebra , Überblick SQL - Transformationen von ER nach Relationen, Normalisierung - XML, DTD, XML-Schema, Info-Set, Namensräume - Metamodelle und Repository - RDF, RDF-S und Ontologien - UML - Petri Netze, Workflownetze - BPMN
Number:	021101000
Type:	Vorlesung
Semester hours:	2 SWS
Link:	C@MPUS
Organisation:	Datenbanken und Informationssysteme

Practical Course Information Systems

Übung
4 SWS
SS 22

Semester:	SS 22
Content:	This practical course mainly focuses on data-intensive applications and how they process, analyze and store their data. Nowadays, the amount of data in applications is growing intensely. This data can be either structured, e.g., stored in relational database systems such as MySQL, or unstructured, e.g., stored in text documents, pictures or other media files. Deriving information and, as a consequence, knowledge from this data is a huge challenge. To cope with this issue, new data analytics and data storage technologies have been developed that focus on the one hand on deriving information from this large amount of structured and unstructured data (e.g., data mining, natural language processing, ...) and on the other hand on efficient data storage (e.g., NoSQL databases, column-stores, ...). Modern web technologies are used to provide the web frontend and to visualize huge data sets and analytic results. Furthermore, to enable fast, efficient application development as well as a high degree of accessibility, flexibility and scalability, cloud computing platforms are oftentimes used to implement such applications. In this practical course, you will work in small teams. Each team will implement a new or extend an existing data-intensive application based on tasks that typically involve data analysis, data visualization, data storage and efficient data processing. The used infrastructure, e.g. a cloud-based infrastructure like IBM Cloud, depends on the application.
Number:	021188000
Type:	Übung
Semester hours:	4 SWS
Link:	C@MPUS
Organisation:	Datenbanken und Informationssysteme

Data Science mit Python

Übung
3 SWS
SS 22

Semester:	SS 22
Content:	siehe Vorlesung
Number:	021100105
Type:	Übung
Semester hours:	3 SWS
Link:	C@MPUS
Organisation:	Datenbanken und Informationssysteme

Advanced Information Management

Übung
2 SWS
SS 22

Semester:	SS 22
Content:	Siehe Vorlesung
Number:	021152000
Type:	Übung
Semester hours:	2 SWS
Link:	C@MPUS
Organisation:	Datenbanken und Informationssysteme

Industrial Analytics

Übung
1 SWS
SS 22

Semester:	SS 22
Content:	siehe Vorlesung
Number:	021100305
Type:	Übung
Semester hours:	1 SWS
Link:	C@MPUS
Organisation:	Datenbanken und Informationssysteme

Modellierung

Übung
2 SWS
SS 22

Semester:	SS 22
Content:	Die Vorlesung vermittelt wie wesentliche Artefakte eines IT Systems modelliert werden, den Zusammenhang und das Zusammenspiel solcher Artefakte sowie die Rolle von Metamodellen und deren Erstellung. Die Vorlesung beinhaltet folgende Themen: - Entity-Relationship Modell und komplexe Objekte - Relationenmodell und Relationenalgebra , Überblick SQL - Transformationen von ER nach Relationen, Normalisierung - XML, DTD, XML-Schema, Info-Set, Namensräume - Metamodelle und Repository - RDF, RDF-S und Ontologien - UML - Petri Netze, Workflownetze - BPMN
Number:	021102000
Type:	Übung
Semester hours:	2 SWS
Link:	C@MPUS
Organisation:	Datenbanken und Informationssysteme

Additional current courses

Here, we provide information on additional courses that are not listed in Campus.

Study projects: currently not available
Development projects: currently not available
Project Data Science: Summer term 2022

This overview shows the courses typically offered by the department in one year.

Bachelor

Einführung in Data Science: Lecture in the winter term (in German)
Modellierung: Lecture in the summer term (in German)
Seminars and Projects: Seminars, projects and similar courses are offered on a irregular basis (typically in German)

Master

Datenbanken und Informationssysteme: Lecture in the winter term (in German)
Data Warehouse, Data Mining and OLAP: Lecture in the winter term (in English)
Advanced Information Management: Lecture in the summer term (in English)
Vertiefungslinie Datenbanken und Informationssysteme: Among others, the lectures Datenbanken und Informationssysteme, Data Warehouse, Data Mining and OLAP and Advanced Information Management are part of this Vertiefungslinie
Advanced seminars, practical courses, projects: Advanced seminars, practical courses, projects and similar courses are offered on a irregular basis (Language: German or English)

Open and Current Student Projects

Student projects in international study programmes

Exams

Information on exams offered by the department

Stuttgart Data Management Academy

Teaching

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