Advanced Data Management, Big Data and Business Intelligence

11M2008

Master

German

5.0

only winter term

1 semester

The increasing (free) availability of data from various sources (sensors, web, e-commerce, social media, open data) places new demands on the (distributed) storage and processing of large, polystructured data volumes in a short space of time. Relational database management systems (including current ones) are reaching their limits here. In this module, current research results are therefore examined and selected technologies are practiced on the basis of real practical and research-relevant issues. This will enable students to introduce current big data technologies into their professional practice and to carry out further scientific research in the subject area.

The subject area of advanced data management is subject to technological change in short periods of time. The following list of topics is therefore continuously updated:

Advanced database development approaches
 1. scaling approaches 
 2. tuning of database applications 
 3. data protection and data security
 4. in-memory databases
Distributed and parallel database management systems
 1. distributed ledger technologies
 2. big data infrastructures and their components 
 3. database clusters and architectures
 4. big data ecosystems 
 5. cloud solutions 
 6. appliances 
Post SQL databases at a glance 
 1 NOSQL database systems 
 2. data stream management systems
 3. time series databases and temporal extensions
 4. database extensions
Applications
 1. management information systems 
 2. knowledge discovery in databases 
 3. big data in various application areas  
 4. (International) data rooms
 5. research data management

5. Current scientific developments
6. Use cases

The total workload for the module is 150 hours (see also "ECTS credit points and grading").

• Homework / Assignment

• Field work / Experimental work or
• Regular participation

The selection of graded and ungraded examination types from the specified options is the responsibility of the respective teacher. In doing so, the teacher must adhere to the applicable study regulations.

Graded examination component:

• Term paper – approx. 15 pages, accompanying presentation approx. 10 minutes

Non-graded examination component:

• Experimental work: Experiment: approx. 5 experiments in total
• Regular attendance: Attendance of at least 80% of the course

This module requires knowledge of database development (design, CRUD, transactions, etc.), which is typically taught in a basic module "Databases" in the bachelor's program. Students who do not have this prior knowledge are encouraged to acquire the necessary prior knowledge using, for example, a standard database textbook and suitable practical tasks. The teacher will also be happy to provide relevant material upon request.

Students who have successfully studied this subject are familiar with current data management technologies and approaches as well as their areas of application. They are able to create practice-oriented examples.

Students also have extensive specialist knowledge of practical applications of data-integrating, storing and analyzing systems, taking volume, variety and velocity into account. Students are familiar with current reference architectures and framework recommendations for data protection and data security and are able to critically reflect on them.

Graduates are able to apply their knowledge, understanding and problem-solving skills in new and unfamiliar situations that have a broader or multidisciplinary connection to their field of study.

Students are able to use modern approaches to data management as part of complex IT projects and combine their application with previously acquired skills. They can learn new technologies with guidance and place them in the domain context. They carry out research and development projects within a defined framework and implement them as prototypes.

Students are able to present current research results to a specialist audience in formal presentations. They are able to engage in critical discussions with users, software developers and data scientists.

• Tapken, Heiko

• Tapken, Heiko