Hasso-Plattner-Institut
Prof. Dr. Felix Naumann
  
 

Julian Risch

I am a Ph.D. student at the Information Systems Group and a member of the HPI Research School. My research focuses on topic modeling and deep learning with applications in the field of comment analysis. Further, I am involved in projects on patent classification and book recommendation.

Contact Information

Prof.-Dr.-Helmert-Straße 2-3
D-14482 Potsdam
Room: F-2.08

Phone: +49 331 5509 272

Email: Julian Risch

Open Master's Theses

I provide supervision for Master's theses in the area of News Comment Analysis, e.g., Toxic Comment Classification, User Engagement Prediction, Comment Recommendation, and Discussion Summarization/Visualization. Feel free to schedule an informal meeting with me to discuss details of these topics and/or your own ideas.

Teaching

Publications

Approximate Discovery of Functional Dependencies for Large Datasets

Bleifuß, Tobias; Bülow, Susanne; Frohnhofen, Johannes; Risch, Julian; Wiese, Georg; Kruse, Sebastian; Papenbrock, Thorsten; Naumann, Felix in Proceedings of the International Conference on Information and Knowledge Management (CIKM) page 1803-1812 . New York, NY, USA , ACM , 2016 .

Functional dependencies (FDs) are an important prerequisite for various data management tasks, such as schema normalization, query optimization, and data cleansing. However, automatic FD discovery entails an exponentially growing search and solution space, so that even today’s fastest FD discovery algorithms are limited to small datasets only, due to long runtimes and high memory consumptions. To overcome this situation, we propose an approximate discovery strategy that sacrifices possibly little result correctness in return for large performance improvements. In particular, we introduce AID-FD, an algorithm that approximately discovers FDs within runtimes up to orders of magnitude faster than state-of-the-art FD discovery algorithms. We evaluate and compare our performance results with a focus on scalability in runtime and memory, and with measures for completeness, correctness, and minimality.
[ URL ] [ DOI ]
Approximate Discovery of ... - Download
Further Information
Tags approximate  discovery  functional_dependencies  hpi  isg  profiling