Hasso-Plattner-Institut
Prof. Dr. Felix Naumann
  
 

Thorsten Papenbrock

Research Assistant, PhD Candidate

Hasso-Plattner-Institut
für Softwaresystemtechnik
Prof.-Dr.-Helmert-Straße 2-3
D-14482 Potsdam
Room: G-3.1.09

 

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


Projects

Metanome

Research Interests

Data Profiling:

Solving computationally complex tasks is a challenge and a central activity in data profiling. This involves primarily the discovery of metadata in many gigabyte-sized datasets, which is why algorithms developed for this purpose need to be efficient and robust. Because data profiling offers such a plethora of challenging, yet unsolved tasks, I have chosen it as my primary research area. I am in particular interested in the discovery of data dependencies, such as inclusion dependencies, unique column combinations, functional dependencies, order dependencies, matching dependencies, and many more.

Data Cleansing:

Data is one of the most important assets in any company. Therefore, it is crucial to ensure its quality and reliability. Data cleansing and data profiling are two essential tasks that - if performed correctly and frequently - help to guarantee data fitness. In this area, I am particularly interested in (semi-)automatic duplicate detection methods and normalization techniques as well as their efficient implementation.

Parallel and Distributed Systems:

Due to the complexity of many tasks in IT, a clever algorithm alone is often not able to deliver a solution in time. In these cases, parallel and distributed systems are needed. Especially when facing ever larger datasets, i.e., big data, we need to consider technologies such as map-reduce (e.g. Spark and Flink), actors (e.g. Akka), and GPUs (e.g. CUDA and OpenCL) to implement scalability into our solutions.

Teaching

Lectures:

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

Seminars:

  • Advanced Data Profiling (2013, 2017)
  • Proseminar Information Systems (2014)

Bachelor Projects:

  • Data Refinery - Scalable Offer Processing with Apache Spark (2015/2016)

Master Projects:

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

Master Thesis:

    • Discovering Matching Dependencies (Andrina Mascher, 2013)
    • Discovery of Conditional Unique Column Combination (Jens Ehrlich, 2014)
    • Spinning a Web of Tables through Inclusion Dependencies (Fabian Tschirschnitz, 2014)
    • Multivalued Dependency Detection (Tim Draeger, 2016)
    • Discovery Algorithms for Conditional Functional Dependencies (Maximilian Grundke, 2017)
    • Discovering Matching Dependencies (Philipp Schirmer, 2017)

    Online Courses:

    • Datenmanagement mit SQL (openHPI, 2013)

    Publications

    A Hybrid Approach for Efficient Unique Column Combination Discovery

    Papenbrock, Thorsten; Naumann, Felix in Proceedings of the conference on Database Systems for Business, Technology, and Web (BTW) page 195-204 . 2017 .

    Unique column combinations (UCCs) are groups of attributes in relational datasets that contain no value-entry more than once. Hence, they indicate keys and serve data management tasks, such as schema normalization, data integration, and data cleansing. Because the unique column combinations of a particular dataset are usually unknown, UCC discovery algorithms have been proposed to find them. All previous such discovery algorithms are, however, inapplicable to datasets of typical real-world size, e.g., datasets with more than 50 attributes and a million records. We present the hybrid discovery algorithm HyUCC, which uses the same discovery techniques as the recently proposed functional dependency discovery algorithm HyFD: A hybrid combination of fast approximation techniques and efficient validation techniques. With it, the algorithm discovers all minimal unique column combinations in a given dataset. HyUCC does not only outperform all existing approaches, it also scales to much larger datasets.
    paper.pdf
    Further Information
    Tags discovery hpi hyucc isg parallel profiling unique_column_combinations