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

Scaling Out the Discovery of Inclusion Dependencies

Kruse, Sebastian; Papenbrock, Thorsten; Naumann, Felix in Proceedings of the conference on Database Systems for Business, Technology, and Web (BTW) page 445-454 . 2015 .

Inclusion dependencies are among the most important database dependencies. In addition to their most prominent application – foreign key discovery – inclusion dependencies are an important input to data integration, query optimization, and schema redesign. With their discovery being a recurring data profiling task, previous research has proposed different algorithms to discover all inclusion dependencies within a given dataset. However, none of the proposed algorithms is designed to scale out, i.e., none can be distributed across multiple nodes in a computer cluster to increase the performance. So on large datasets with many inclusion dependencies, these algorithms can take days to complete, even on high-performance computers. We introduce SINDY, an algorithm that efficiently discovers all unary inclusion dependencies of a given relational dataset in a distributed fashion and that is not tied to main memory requirements. We give a practical implementation of SINDY that builds upon the map-reduce-style framework Stratosphere and conduct several experiments showing that SINDY can process huge datasets by several factors faster than its competitors while scaling with the number of cluster nodes.
Scaling Out the Discovery... - Download
Further Information
Tags discovery  disctributed  inclusion_dependencies  isg  parallel  profiling