Juniorprofessur

Datenbanken und Informationssysteme

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Data Management and Processing Technologies for Data Science

The steadily increasing informatization of society and economy produces data at a rate that has never been seen before. The volume and variety of available digital information continuously inspires new possibilities how insights can be gained by analyzing this data. In order to realize this potential, numerous research efforts are already underway, which are typically summarized under the umbrella of data science. Data science is a field that crosscuts many research area of computer science, such as artificial intelligence, machine learning, data mining, databases, and information systems. Our research falls into the last two of these areas and aims at supporting data science at the system level. Data science requires the management of new types of data as well as new complex ways to process it. Our research method is to address these requirements by innovating new and general solutions that leverage and extend core database and information systems technologies. Within this broad area, our research focuses on challenges linked to data processing, in both traditional database and data stream management systems.

Recent Publications

  • Andreas Weiler, Michael Grossniklaus, and Marc H. Scholl: Survey and Experimental Analysis of Event Detection Techniques for Twitter, Oxford Computer Journal (to appear)
  • Manuel Stein, Halldòr Janetzko, Thorsten Breitkreutz, Daniel Seebacher, Tobias Schreck, Michael Grossniklaus, Iain Couzin, and Daniel A. Keim: Director’s Cut: Analysis and Annotation of Soccer Matches, IEEE Computer Graphics and Applications (to appear)
  • Alexander Bergmayr, Michael Grossniklaus, Manuel Wimmer, and Gerti Kappel: Leveraging Annotation-based Modeling with JUMPSoftware & Systems Modeling (to appear)
  • Andreas Weiler, Michael Grossniklaus, and Marc H. Scholl: An Evaluation of the Run-time and Task-based Performance of Event Detection Techniques for TwitterInformation Systems (to appear)
  • Andreas Weiler, Joeran Beel, Bela Gipp, and Michael Grossniklaus: Stability Evaluation of Event Detection Techniques for Twitter, Proceedings of IDA 2016, 15th International Symposium on Intelligent Data Analysis, Stockholm, Sweden, October 2016
  • Jürgen Hölsch, Michael Grossniklaus, and Marc H. Scholl: Optimization of Nested Queries using the NF2 Algebra. In Proceedings of SIGMOD 2016, International Conference on Management of Data, San Francisco, CA, USA, June/July 2016
  • Michael Grossniklaus, David Maier, James Miller, Sharmadha Moorthy, and Kristin Tufte: Frames: Data-Driven Windows. In Proceedings of DEBS 2016, International Conference on Distributed and Event-Based Systems, Irvine, CA, USA, June 2016
  • Michael Grossniklaus, Marc H. Scholl, and Andreas Weiler: Towards Adaptive Event Detection Techniques for the Twitter Social Media Data StreamIEEE Data Engineering Bulletin, Vol. 38, No. 4
  • Andreas Weiler, Michael Grossniklaus, and Marc H. Scholl: Situation Monitoring of Urban Areas Using Social Media Data StreamsInformation Systems, Vol. 57, April 2016
  • Jürgen Hölsch and Michael Grossniklaus: An Algebra and Equivalences to Transform Graph Patterns in Neo4j. In Proceedings of GraphQ 2016, EDBT Workshop on Querying Graph Structured Data, Bordeaux, France, March 2016
  • Leonard Wörteler, Michael Grossniklaus, Christian Grün, and Marc H. Scholl: Function Inlining in XQuery 3.0 Optimization. In Proceedings of DBPL 2015, 15th International Symposium on Database Programming Languages, Pittsburgh, PA, USA, October 2015
  • Andreas Weiler, Michael Grossniklaus, and Marc H. Scholl: Evaluation Measures for Event Detection Techniques on Twitter Data Streams. In Proceedings of BICOD 2015, 30th British International Conference on Databases, Edinburgh, Scotland, July 2015
  • Andreas Weiler, Michael Grossniklaus, and Marc H. Scholl: Run-time and Task-based Performance of Event Detection Techniques for Twitter. In Proceedings of CAiSE 2015, 27th International Conference on Advanced Information Systems Engineering, Stockholm, Sweden, June 2015
  • Lukas Kircher, Michael Grossniklaus, Christian Grün, and Marc H. Scholl: Efficient Structural Bulk Updates on the Pre/Dist/Size XML Encoding. In Proceedings of ICDE 2015, 31st International Conference on Data Engineering, Seoul, Korea, April 2015

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