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

Data-driven Schema Normalization

Papenbrock, Thorsten; Naumann, Felix in Proceedings of the International Conference on Extending Database Technology (EDBT) page 342-353 . 2017 .

Ensuring Boyce-Codd Normal Form (BCNF) is the most popular way to remove redundancy and anomalies from datasets. Normalization to BCNF forces functional dependencies (FDs) into keys and foreign keys, which eliminates duplicate values and makes data constraints explicit. Despite being well researched in theory, converting the schema of an existing dataset into BCNF is still a complex, manual task, especially because the number of functional dependencies is huge and deriving keys and foreign keys is NP-hard. In this paper, we present a novel normalization algorithm called Normalize, which uses discovered functional dependencies to normalize relational datasets into BCNF. Normalize runs entirely data-driven, which means that redundancy is removed only where it can be observed, and it is (semi-)automatic, which means that a user may or may not interfere with the normalization process. The algorithm introduces an efficient method for calculating the closure over sets of functional dependencies and novel features for choosing appropriate constraints. Our evaluation shows that Normalize can process millions of FDs within a few minutes and that the constraint selection techniques support the construction of meaningful relations during normalization.
[ URL ] [ DOI ]
Data-driven Schema Normal... - Download
Further Information
Tags data-driven  functional_dependencies  hpi  hyfd  isg  normalization  profiling  schema  unique_column_combinations