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

  • Complex data engineering problems

  • Parallel and distributed computing challenges

    • e.g. robustness, efficiency, and elasticity

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

Progressive Duplicate Detection

Papenbrock, Thorsten; Heise, Arvid; Naumann, Felix in IEEE Transactions on Knowledge and Data Engineering (TKDE) 2015 .

Duplicate detection is the process of identifying multiple representations of same real world entities. Today, duplicate detection methods need to process ever larger datasets in ever shorter time: maintaining the quality of a dataset becomes increasingly difficult. We present two novel, progressive duplicate detection algorithms that significantly increase the efficiency of finding duplicates if the execution time is limited: They maximize the gain of the overall process within the time available by reporting most results much earlier than traditional approaches. Comprehensive experiments show that our progressive algorithms can double the efficiency over time of traditional duplicate detection and significantly improve upon related work.
[ URL ] [ DOI ]
Progressive Duplicate Det... - Download
Further Information
Tags data_cleansing  duplicate_detection  hpi  isg