Hasso-Plattner-Institut
  
Hasso-Plattner-Institut
Prof. Dr. Felix Naumann
  
 

Sebastian Kruse

Research Assistant at Information Systems Group

Contact

Hasso-Plattner-Institut für Softwaresystemtechnik
Prof.-Dr.-Helmert-Straße 2-3
D-14482 Potsdam, Germany

Phone: ++49 331 5509 240
Fax: ++49 331 5509 287
Room: 2-01.2, Building E (formerly "Hinterer Neubau")
Email: Sebastian Kruse

Research Interests

  • Data profiling
  • Distributed systems
  • Map/Reduce frameworks
  • Query optimization
  • Cross-platform/polyglot data processing

Projects

Teaching

Master's Theses

  • Estimating Metadata of Query Results using Histograms (Cathleen Ramson, 2014)
  • Quicker Ways of Doing Fewer Things: Improved Index Structures and Algorithms for Data Profiling (Jakob Zwiener, 2015)
  • Methods of Denial Constraint Discovery (Tobias Bleifuß, 2016)

Seminars

Master Projects

  • Approximate Data Profiling (SS 15)

Bachelor Projects

Professional Activities

  • Member of GI (since 2015) and ACM (since 2016)
  • Reviewer for Information Systems Journal
  • Contributor to Apache Flink

Publications

Divide & Conquer-based Inclusion Dependency Discovery

Thorsten Papenbrock, Sebastian Kruse, Jorge-Arnulfo Quiane-Ruiz, Felix Naumann
Proceedings of the VLDB Endowment, vol. 8(7):774-785 2015

Abstract:

The discovery of all inclusion dependencies (INDs) in a dataset is an important part of any data profiling effort. Apart from the detection of foreign key relationships, INDs can help to perform data integration, query optimization, integrity checking, or schema (re-)design. However, the detection of INDs gets harder as datasets become larger in terms of number of tuples as well as attributes. To this end, we propose BINDER, an IND detection system that is capable of detecting both unary and n-ary INDs. It is based on a divide & conquer approach, which allows to handle very large datasets – an important property on the face of the ever increasing size of today’s data. In contrast to most related works, we do not rely on existing database functionality nor assume that inspected datasets fit into main memory. This renders BINDER an efficient and scalable competitor. Our exhaustive experimental evaluation shows the high superiority of BINDER over the state-of-the-art in both unary (SPIDER) and n-ary (MIND) IND discovery. BINDER is up to 26x faster than SPIDER and more than 2500x faster than MIND.

Keywords:

profiling,inclusion dependencies,binder,hpi

BibTeX file

@article{papenbrock2015binder,
author = { Thorsten Papenbrock, Sebastian Kruse, Jorge-Arnulfo Quiane-Ruiz, Felix Naumann },
title = { Divide \& Conquer-based Inclusion Dependency Discovery },
journal = { Proceedings of the VLDB Endowment },
year = { 2015 },
volume = { 8 },
number = { 7 },
pages = { 774-785 },
month = { 0 },
abstract = { The discovery of all inclusion dependencies (INDs) in a dataset is an important part of any data profiling effort. Apart from the detection of foreign key relationships, INDs can help to perform data integration, query optimization, integrity checking, or schema (re-)design. However, the detection of INDs gets harder as datasets become larger in terms of number of tuples as well as attributes. To this end, we propose BINDER, an IND detection system that is capable of detecting both unary and n-ary INDs. It is based on a divide & conquer approach, which allows to handle very large datasets – an important property on the face of the ever increasing size of today’s data. In contrast to most related works, we do not rely on existing database functionality nor assume that inspected datasets fit into main memory. This renders BINDER an efficient and scalable competitor. Our exhaustive experimental evaluation shows the high superiority of BINDER over the state-of-the-art in both unary (SPIDER) and n-ary (MIND) IND discovery. BINDER is up to 26x faster than SPIDER and more than 2500x faster than MIND. },
keywords = { profiling,inclusion dependencies,binder,hpi },
publisher = { VLDB Endowment },
booktitle = { Proceedings of the International Conference on Very Large Data Bases (PVLDB) },
issn = { 2150-8097 },
priority = { 0 }
}

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last change: Thu, 16 Jul 2015 11:27:32 +0200