Hasso-Plattner-Institut
Prof. Dr. Felix Naumann
  
 

Dr. Thorsten Papenbrock

Senior Researcher
Head of the Distributed Computing group

Hasso-Plattner-Institut
für Softwaresystemtechnik
Prof.-Dr.-Helmert-Straße 2-3
D-14482 Potsdam
Office: F-2.04, Campus II

 

Phone: +49 331 5509 294
Email:  thorsten.papenbrock(a)hpi.de
Profiles: Xing, LinkedIn
Research: ORCID, GoogleScholar, DBLP, ResearchGate

Dissertation: Data Profiling - Efficient Discovery of Dependencies


Projects

Metanome

Research Interests

Technology Interests

  • Data flow engines

  • Message passing systems

  • Parallel hardware toolkits

Teaching

Lectures:

  • Distributed Data Management (2018, 2019)
  • 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:

  • Reliable Distributed Systems Engineering (2019)
  • Mining Streaming Data (2019)
  • Actor Database Systems (2018)
  • Proseminar Information Systems (2014)
  • Advanced Data Profiling (2013, 2017)

Bachelor Projects:

  • 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:

  • 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)

Publications

Black Swan: Augmenting Statistics with Event Data

Lorey, Johannes; Naumann, Felix; Forchhammer, Benedikt; Mascher, Andrina; Retzlaff, Peter; ZamaniFarahani, Armin; Discher, Soeren; Faehnrich, Cindy; Lemme, Stefan; Papenbrock, Thorsten; Peschel, Robert Christoph; Richter, Stephan; Stening, Thomas; Viehmeier, Sven in Proceedings of the 20th Conference on Information and Knowledge Management (CIKM) page 2517-2520 . Glasgow, UK , 2011 .

A large number of statistical indicators (GDP, life expectancy, income, etc.) collected over long periods of time as well as data on historical events (wars, earthquakes, elections, etc.) are published on the World Wide Web. By augmenting statistical outliers with relevant historical occurrences, we provide a means to observe (and predict) the influence and impact of events. The vast amount and size of available data sets enable the detection of recurring connections between classes of events and statistical outliers with the help of association rule mining. The results of this analysis are published at http://www.blackswanevents.org and can be explored interactively.
Black Swan: Augmenting St... - Download
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