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Teaching

Find all information about lectures, seminars, internships, projects and thesis topics.

  • Lectures

    SS 2023

    Algorithms and Datastructures
    NoSQL Database Systems

    WS 2022/23:

    Spatial Databases
    Implementation of Database Systems

    SS 2022:

    Database systems

    WS 2021/22:

    Information retrieval
    Digitization as a civilizing process

  • Seminars

    WS 2021/22:

    Standardization and governance in digital economies

    WS 2020/21:

    Computer science as a driver of social change

    SS 2020:

    Big data management and analytics
    Graphtechnologies

  • Internships

    An internship (Fortgeschrittenenpraktikum, FoPra) can be supervised by one of our of research assistants. Topics will be associated with the assistant's field of study. We always have a couple of open topics, but individual suggestions from students are also welcome. If you are interested in doing an internship in one of the following areas, please contact the respective supervisor.

    Data Stream Storage & Processing (Supervision: Nikolaus Glombiewski, Dominik Brandenstein)
    Spatio-Temporal Data Analysis and Event Stream Processing (Supervision: Andreas Morgen)
    Algorithm Engineering for Time Series (Supervision: Jana Holznigenkemper)
    Implementation of data structures on DNA (Supervision: Alex El-Shaikh)
    Concurrency Control for B-Trees (Supervision: Amir El-Shaikh)
    Multi Version Systems (Supervision: Amir El-Shaikh)

    For more general information on our ongoing research, you can also visit the Research page.

  • Projects

    WS 2019/2020

    Cryogenics Monitoring@CERN
    Master students (2 Terms)

    CERN (European Organization for Nuclear Research) operates the Large Hadron Collider (LHC), the world's largest particle accelerator. The accelerator ring has a circumference of more than 26 km and must be cooled down to approx. 2° K (-271°C) during operation. If the operating temperature cannot be maintained, an experiment must be aborted prematurely, resulting in a time-consuming and costly restart.

    Along the accelerator a series of so-called beam screens are installed to monitor the temperature of the system. In this project, a system is to be developed that analyses the measurement data of the beam screens in (near) real time and generates an alarm in critical situations.
    The team will evaluate a number of open-source projects (e.g. Apache Flink, Spark Streaming) and determine which one is best suited for the above scenario. Subsequently, a complete processing pipeline will be developed on the basis of the selected system, which receives input data from various sources and reports the generated alarms to defined interfaces.

  • Thesis topics

    A Bachelor or Master thesis can be supervised by one of our of research assistants. Topics will be associated with the assistant's field of study. We always have a couple of open topics, but individual suggestions from students are also welcome. If you are interested in writing a thesis paper in one of the following areas, please contact the respective supervisor.

    Data Stream Storage & Processing (Supervision: Nikolaus Glombiewski, Dominik Brandenstein)
    Spatio-Temporal Data Analysis and Event Stream Processing (Supervision:Andreas Morgen And)
    Algorithm Engineering for Time Series (Supervision: Jana Holznigenkemper)
    Implementation of data structures on DNA (Supervision: Alex El-Shaikh)
    Concurrency Control for B-Trees (Supervision: Amir El-Shaikh)
    Multi Version Systems (Supervision: Amir El-Shaikh)

    For more general information on our ongoing research, you can also visit the Research page.

    Here you can find an extract of thesis topics of the last years: 

    Bachelor thesis:

    - Iterator-Based Processing of Raster Time Series
    - Persistent Homology
    - Visual Clustering of Large Heterogeneous Point Sets 
    - Detection of hot spots in time series
    - Parallelizing the DBScan clustering algorithm
    - Parallel event stream aggregation in Rust
    - Indexing time series in Apache Kafka
    - Parallel join processing on event streams in Rust
    - Indexing intervals in time series databases

    Master thesis:

    - Detection of complex movement patterns in data streams
    - Complex event processing over uncertain event streams
    - Caching of spatio-temporal data
    - Dynamic lightweight indexing for event streams
    - Implementing time series databases in Rust
    - Replication for time series databases with the Raft consensus algorithm
    - Indexing strategies for pattern matching queries