Prof. Johann-Christoph Freytag (PhD)


E-Mail Address:

Field of research:

Multi-Modal Similarity Search on Web-Scale Data


Johann-Christoph Freytag is currently full professor for Databases and Information Systems (DBIS) at the Computer Science Department of the Humboldt-Universität zu Berlin, Germany. Before joining the department in 1994, he was a research staff member at the IBM Almaden Research Center (1985-1987), a researcher at the European Computer-Industry-Research Centre (ECRC, in Munich, Germany, 1987-1989), and the head of Digital’s Database Technology Center (also in Munich, 1990-1993). He holds a Ph.D. in Applied Mathematics/Computer Science from Harvard University, MA.

Role at the HEADT Centre:

Principal Investigator, Researcher

Fields of Interest:

Prof. Freytag’s research interests include all aspects of query processing and query optimization in object-relational database systems, new developments in the database area (such as semi-structured data, data quality, databases and security), privacy in database systems, and applying database technology to applications such as GIS, genomics, and bioinformatics/life science. In the last years he received the IBM Faculty Award four times for collaborative work in the areas of databases, middleware, and bioinformatics/life science.


  • Alexandrov, Alexander, Rico Bergmann, Stephan Ewen, Johann-Christoph Freytag, Fabian Hueske, Arvid Heise, Odej Kao, et al. 2014. “The Stratosphere Platform for Big Data Analytics.” The VLDB Journal 23 (6): 939–64.
  • Fier, Fabian, Eva Höfer, and Johann-Christoph Freytag. 2016. “MapReduce Frameworks: Comparing Hadoop and HPCC.” In Proceedings of the Conference “Lernen, Wissen, Daten, Analysen”, Potsdam, Germany, September 12-14, 2016, edited by Ralf Krestel, Davide Mottin, and Emmanuel Müller, 1670:194–99. CEUR Workshop Proceedings.
  • Hueske, Fabian, Mathias Peters, Aljoscha Krettek, Matthias Ringwald, Kostas Tzoumas, Volker Markl, and Johann-Christoph Freytag. 2013. “Peeking into the Optimization of Data Flow Programs with MapReduce-Style UDFs.” In 29th International Conference on Data Engineering (ICDE), 2013 IEEE, 1292–95. Brisbane, Australia: IEEE.

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