Modulcode:	infBDMaA-01a
Englische Bezeichnung:	Big Data Management and Analytics
Modulverantwortliche(r):	Prof. Dr. Peer Kröger
Turnus:	jedes Jahr im WS (WS20/21 WS21/22 WS22/23 WS23/24)
Präsenzzeiten:	4V 2Ü
ECTS:	8
Workload:	60 h lectures, 30 h exercises, 150 h self studies
Dauer:	ein Semester
Modulkategorien:	BSc-Inf-WP (BSc Inf (21)) BSc-WInf-WP-Inf (BSc WInf (21)) MSc-Inf-WP (MSc Inf (21)) 2F-MEd-Inf-WP (MEd-Hdl Inf (21)) 2F-MA-Inf-WP (2F-MA Inf (21)) MSc-WInf-WP-Inf (MSc WInf (21)) WI (MSc Inf (15)) WI (MSc WInf (15))
Lehrsprache:	Englisch
Voraussetzungen:	Inf-ADS Inf-IS

Kurzfassung:

Big Data Management and Analytics, i.e. integrating and managing big, heterogeneous data for the purpose of gaining interesting information from that data through suitable analyses, is a key technology in all sectors of a data intensive world. The aim of the course is to provide a fundamental understanding of important concepts, algorithms, techniques and architectures of systems and tools for managing and analyzing Big Data.

Lernziele:

After completing the course, students should

• have a basic overview over techniques for managing and analyzing Big Data including capabilities and limitations of the different approaches
• be able to apply suitable Big Data management techniques and Big Data analytics techniques to new problems.

Lehrinhalte:

Selected content from the following general topics:

• Characteristics of Big Data applications
• Data Warehouses (e.g. OLAP)
• Distributed Data Management and Analytics (e.g. NoSQL, Apache Flink)
• Distributed File Systems (e.g. HDFS, Map-Reduce)
• Data Stream Processing
• Representation Learning (Deep Learning) for heterogeneous data

Since the field of managing and analyzing Big Data is currently a very volatile field with high innovation pace, this modul will cover current techniques and aspects of the listed topics.

Weitere Voraussetzungen:

Students should have profound knowledge from the following basic moduls:

• Informationssysteme
• Algorithmen und Datenstrukturen (ADS)
• Knowledge Discovery and Data Mining

Prüfungsleistung:

• Regular submission of and passing the laboratory/practical assignments
• Written exam

Lehr- und Lernmethoden:

Beamer presentation and use of software tools.

Verwendbarkeit:

Literatur:

Since Big Data is a quite new and hot topic, standards and basic concepts are quite dynamic. Thus, relevant literature will be given in the lecture.

Verweise:

Kommentar: