Metanome

• Complex data engineering problems
  • e.g. data profiling, data cleaning, and data integration
• Parallel and distributed computing challenges
  • e.g. robustness, efficiency, and elasticity

• Data flow engines
  • e.g. map-reduce derivatives (Spark and Flink)
• Message passing systems
  • e.g. actor model toolkits (Akka and Orleans) or message queues (Kafka)
• Parallel hardware toolkits
  • e.g. GPU programming libraries (CUDA and OpenCL)

Lectures:

• Distributed Data Management (2018, 2019, 2020)
• Distributed Data Analytics (2017)
• Data Profiling (2017)
• Information Integration (2015)
• Data Profiling and Data Cleansing (2014)
• Database Systems I (2013, 2014, 2015, 2016, 2017)
• Database Systems II (2013)

Seminars:

• Sustainable Machine Learning on Edge Device Clusters (2020)
• Machine Learning for Data Streams (2019)
• Reliable Distributed Systems Engineering (2019)
• Mining Streaming Data (2019)
• Actor Database Systems (2018)
• Proseminar Information Systems (2014)
• Advanced Data Profiling (2013, 2017)

Bachelor Projects:

• UltraMine - Scalable Analytics on Time Series Data (2020/2021)
• Data Refinery - Scalable Offer Processing with Apache Spark (2015/2016)

Master Projects:

• Profiling Dynamic Data - Maintaining Matadata under Inserts, Updates, and Deletes (2016)
• Approximate Data Profiling - Efficient Discovery of approximate INDs and FDs (2015)
• Metadata Trawling - Interpreting Data Profiling Results (2014)
• Joint Data Profiling - Holistic Discovery of INDs, FDs, and UCCs (2013)

Master Thesis:

• A2DB: A Reactive Database for Theta-Joins (Julian Weise, 2020)
• Distributed Detection of Sequential Anomalies in Time Related Sequences (Johannes Schneider, 2020)
• Efficient Distributed Discovery of Bidirectional Order Dependencies (Sebastian Schmidl, 2020)
• Distributed Unique Column Combination Discovery (Benjamin Feldmann, 2019)
• Reactive Inclusion Dependency Discovery (Frederic Schneider, 2019)
• Inclusion Dependency Discovery on Streaming Data (Alexander Preuss, 2019)
• Generating Data for Functional Dependency Profiling (Jennifer Stamm, 2018)
• Efficient Detection of Genuine Approximate Functional Dependencies (Moritz Finke, 2018)
• Efficient Discovery of Matching Dependencies (Philipp Schirmer, 2017)
• Discovering Interesting Conditional Functional Dependencies (Maximilian Grundke, 2017)
• Multivalued Dependency Detection (Tim Draeger, 2016)
• Spinning a Web of Tables through Inclusion Dependencies (Fabian Tschirschnitz, 2014)
• Discovery of Conditional Unique Column Combination (Jens Ehrlich, 2014)
• Discovering Matching Dependencies (Andrina Mascher, 2013)

Online Courses:

• Datenmanagement mit SQL (openHPI, 2013)

• Data Profiling at Scale (HPI 2019)
• A Hybrid Approach to Functional Dependency Discovery (SIGMOD 2016)
• Holistic Data Profiling: Simultaneous Discovery of Various Metadata (EDBT 2016)
• Functional Dependency Discovery: An Experimental Evaluation of Seven Algorithms (VLDB 2015)
• Divide & Conquer-based Inclusion Dependency Discovery (VLDB 2015)