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DIMA Studienmodule

DIMA offeriert Vorlesungen für verschiedene Master Studienprogramme, z.B. Informatik, Wirtschaftsinformatik, Technische Informatik und Wirtschaftsingenieurwesen sowie für die internationalen Programme Erasmus Mundus IT4BI Master [1] und EIT Digital Data Science Master [2].

Neben einem soliden theoretischen Fundament und praktischen, berufsrelevanten Inhalten, legen wir großen Wert auf die Einbettung von aktuellen Trends und Forschungsfragestellungen in den Lehrstoff.

Im Masterstudium beinhaltet das Lehrangebot von DIMA auch die Vermittlung von sog. Softskills durch Projektarbeit und Seminare, mit explizitem Fokus auf Teamarbeit, Internationalität und wissenschaftlichem Arbeiten. 

Die ausführlichen, aktuellen Beschreibungen können in der zentralen Moduldatenbank [3] in MOSES abgerufen werden, ebenso die Modullisten [4] mit den zu belegenden Pflicht-, Wahlpflicht- und Wahlmodulen Ihres Studiengangs.

Hier finden Sie eine Kurzbeschreibung unserer Module im Masterstudium mit der Ausrichtung Informationsmanagement (IMA).

Basic Master Courses


Database Technology


Course Language: English

The database technology course is split into two parts, each covering roughly one half of the semester. During the first part, the students learn the fundamentals of database technology for relational database systems. This includes the general architecture of a DBMS, file- and buffer management, query processing, indexing, metadata management, query optimization, locking, recovery and transaction management. In the second part, students learn the basics of parallel data processing, with a focus on largescale, distributed systems and “cloud computing”. Topics include parallel relational databases, parallel no-SQL processing platforms like MapReduce, distributed data storage and retrieval – e.g., via DHTs –, techniques for distributed locking and transaction handling, multi-tenancy and software as a service, as well as modern hardware, benchmarking, and data stream processing. The course consists of a lecture and theoretical, written exercises.

MOSES Course Description [5] (SS 2017)


Implementation of a Database Engine (Database Technology Lab)


Course Language: English

The global data volume is increasing dramatically each year. Understanding how to store, process and manage these huge amounts of data efficiently is a key requirement for software engineers and data analysts in the modern IT world. This lab (following the corresponding lecture topics of DBT-Database Technology) will teach students both the fundamentals of data processing in traditional single-node database systems and how to scale out these techniques to huge amounts of data in large-scale, distributed environments. During the implementation part of the lab, students will get hands-on experience with important data processing techniques by implementing several components of a relational database system and by using parallel programming platforms like Apache Hadoop or Nephele/PACT. In the database technology lab, students will implement components of a relational database system and get hands-on experience with a parallel data processing platform. The actual components implemented may vary each year, but will include parsing, query optimizer, execution engine, index structures and storage system.

MOSES Course Description [6] (SS 2017)

Advanced Information Management Courses


Advanced Information Management 1 (AIM-1) - Heterogeneous and Distributed Information Systems (HDIS)


Course Language: English

The participants of this module will achieve deep conceptual, methodical, technical and practical knowledge in requirements analysis, design, architecture and development of heterogeneous and distributed information systems. This includes firstly classical knowledge about federated databases and mediator-based information systems (tight or loose coupling wrt. the dimensions of distribution, heterogeneity and autonomy). Secondly, different paradigms of heterogeneous information infrastructures and their management (e.g. P2P) and interoperability architectures (‘middleware’) will be investigated. Finally, modern model-based concepts for the development, integration and evolution of arbitrary information infrastructures, and –under this conceptual frame– model, metamodel, and metadata management as well as semantic concepts will be discussed and brought into practical experience by some larger project-like group work.

MOSES Course Description [7] (SS 2017)


Advanced Information Management 2 - Management of Data Streams


Course Language: German

In recent years, advances in hardware technology have facilitated new ways of collecting data continuously. In many applications such as for instance network monitoring, the volume of such data is so large that it may be impossible to store the data on disk. Furthemore, even when the data can be stored, the volume of the incoming data may be so large that it may be impossible to process any particular record more than once. Therefore, many database operations and data analysis algorithms such as for instance filtering, indexing, classification and clustering become significantly more challenging in this context. Through the technological advances in the last few years more and more applications are being created that constantly generate data which is only relevant for a certain time frame. Because of this, this type of application has to be able to handle various streams of data. You will gain conceptual, methodological and practical skills in the area of processing data streams, by using examples from various application areas.

MOSES Course Description [8] (SS 2017)


Advanced Information Management 3 (AIM-3) Scalable Data Science: Systems & Methods (SDSSM)


Course Language: English

The last decade was marked by the digitalization of virtually all aspects of modern society. Today, businesses, government institutions, as well as science and engineering organizations, among others face an avalanche of digital data on a daily basis. In order to derive insight from all of this data, society needs individuals with a strong foundation in scalable data science. In this course students will learn about popular scalable data analysis systems and scalable data analytics methods and gain practical experience in conducting scalable data science. The module will focus on mainstream distributed processing platforms and paradigms and learn how to employ these to solve challenging big data problems using popular data mining methods. Students will learn how to implement and employ varying data mining algorithms, such as Naïve Bayes, K-Means Clustering, and PageRank on varying open-source systems (e.g., Apache Hadoop, Apache Flink).

MOSES Course Description [9] (SS 2017)

In-Memory Databases On Modern Hardware

In-Memory Databases On Modern Hardware


Course Language: German

This course will teach students the fundamentals of efficient data processing in main-memory database systems using techniques optimized for main memory (e.g., column stores and query compilation) and modern processor capabilities (e.g., SIMD-based database algorithms, GPU co-processing). The course is split into two parts, each covering roughly one half of the semester. During the first part, the students learn the fundamentals of cache-efficient storage and processing models. This includes columnar storage and query processing, compression, vector-at-a-time processing, query compilation and transaction processing. In the second part, students learn the basics of parallel data processing on modern CPUs and co-processors (e.g., GPUs) for typical database operators, including optimizations such as SIMD and NUMA-awareness. The course consists of a lecture and theoretical, written exercises.

MOSES Course Description [10] (SS 2017)

Information Management Seminars


Seminar Hot Topics in Information Management


Course Language: English

In this seminar you will learn the comprehensive preparation and presentation of a research topic in English. To achieve this you will have to read and categorize a scientific English text, conduct background research and present your findings. Based on primary literature, provided by the teaching staff, and secondary literature, to be searched and found by the participants under guidance of the teaching staff, using research journals and articles published at important conferences in the areas of databases and information systems, information management, information modelling and model management, etc. conferences such as WWW, VLDB, or SIGMOD, CAiSE, MODELS or ICSE, one particular area of research will be covered in depth throughout the term. The concrete “hot” research topics will be given in the announcement for each term.

MOSES Course Description [11] (SS 2017)


Big Data Analytics Seminar


Course Language: English

Participants of this seminar will acquire knowledge about recent research results and trends in the analysis of web-scale data. Through the work in this seminar, students will learn the comprehensive preparation and presentation of a research topic in this field. In order to achieve this, students will get to read and categorise a scientific paper, conduct background literature research and present as well as discuss their findings. After the course, students will be able to critically read and evaluate scientific publications, and to conduct background research. They will be capable of preparing for and giving oral presentations on research topics for an expert audience, of analyzing the state of the art of a research topic, and of summarizing it in a scientific paper. They should also understand techniques used in the scientific community like peer reviews, conference presentations, and defenses of the findings after their presentation, as well as they should understand methods for large-scale data analytics. The course is principally designed to impart: Technical skills: 50x Methodological skills: 20x System skills: 10x Social skills: 20x

MOSES Course Description [12] (SS 2017)

Information Management Projects


Project Hot Topics in Information Management


Course Language: English

As part of a large system development project the students get the opportunity to systematically analyse, model and develop (problem-oriented) a solution for a hot topic in information management. At the start of the project, you receive a description of the topic and related detailed information. Then the team will determine, with assistance of the teacher, a project environment with the appropriate tools for teamwork, project communication, development environment and testing. Afterwards the problem is analyzed, modeled and broken down into individual components so individual tasks can be derived, which are then processed in smaller teams or individual work of team members. In the weekly project meetings the project team will present the project's progress with milestones reached and define the further steps by also taking the feedback from the teacher into account. At the end of the project, each team delivers a final report, presents a project poster and gives a final presentation with a demonstration of the prototype.

MOSES Course Description [13] (SS 2017)


Big Data Analytics Project


Course Language: English

In this course you will learn to systematically analyze a current issue in the information management area and to develop and implement a problem-oriented solution as part of a team. You will learn to cooperate as team member and to contribute to project organization, quality assurance and documentation. The quality of your solution has to be proven through analysis, systematic experiments and test cases. Examples of IMPRO projects carried out in recent semesters are a tool used to analyse Web 2.0 Forum data, an online multiplayer game for mobile phones, implementation and analysis of new join methods for a cloud computing platform or the development of data mining operations on the massively parallel system Hadoop as part of the Apache open source project Mahout. After the course, students will be able to understand methods for large scale data analytics and to solve large scale data analytics problems. They will be capable of designing and implementing large scale data analytics solutions in a collaborative team.

MOSES Course Description [14] (SS 2017)

Masterarbeit - Themen

DIMA bietet Studenten anspruchsvolle Themen für die Master Thesis an. Diese sind oft an laufende Forschungsprojekte aus verschiedenen Bereichen gebunden. Sie bekommen die Möglichkeit mit nationalen und internationalen Forschungspartnern zusammenzuarbeiten.

Nähere Informationen finden Sie hier [15].



Studienberatung DIMA

Dr. Ralf Kutsche
Di. 12 - 13 Uhr
(nur nach Anmeldung)
Tel.: +49 30 31423555

E-Mail-Anfrage [16]
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