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)

Seminars:

  • Advanced Data Profiling (2013)
  • 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 Dräger, 2016)

    Online Courses:

    • Datenmanagement mit SQL (openHPI, 2013)

    Publications

    Black Swan: Augmenting Statistics with Event Data

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

    Abstract:

    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.

    BibTeX file

    @inproceedings{blackswan,
    author = { Johannes Lorey, Felix Naumann, Benedikt Forchhammer, Andrina Mascher, Peter Retzlaff, Armin ZamaniFarahani, Soeren Discher, Cindy Faehnrich, Stefan Lemme, Thorsten Papenbrock, Robert Christoph Peschel, Stephan Richter, Thomas Stening, Sven Viehmeier },
    title = { Black Swan: Augmenting Statistics with Event Data },
    year = { 2011 },
    pages = { 2517-2520 },
    month = { 0 },
    abstract = { 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. },
    address = { Glasgow, UK },
    booktitle = { Proceedings of the 20th Conference on Information and Knowledge Management (CIKM) },
    priority = { 0 }
    }

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    last change: Fri, 17 Apr 2015 11:38:34 +0200