Machine Learning Coffee seminar: Michael Mathioudakis "Machine Learning for Data Management – and vice versa"

Helsinki region machine learning researchers will start our week by an exciting machine learning talk. The aim is to gather people from different fields of science with interest in machine learning. Porridge and coffee is served at 9:00 and the talk will begin at 9:15.
Speech Recognizer Board, professor Teuvo Kohonen, 1980

Machine Learning for Data Management – and vice versa

Michael Mathioudakis
Professor of Computer Science, University of Helsinki


Research efforts in the areas of Machine Learning (ML) and Data Management (DM) have, to a large degree, run in parallel for many years. DM research focused on the efficient querying of  databases, and led to standardized and widely used database management systems. ML research focused on building predictive models from data, and led to high predictive performance for some particularly difficult tasks (e.g., image recognition). As ML software is used in more and more applications, it’s worth discussing whether we can have for ML systems the kind of standardization we have for DM systems – as well as whether we can use ML to improve DM systems. In this talk, I’ll present one recent paper for each direction from SIGMOD2018 [1] and VLDB2018 [2].

[1] Kraska T, Beutel A, Chi EH, Dean J, Polyzotis N. The case for learned index structures. In Proceedings of the 2018 International Conference on Management of Data 2018 May 27 (pp. 489-504). ACM.

[2] Hasani S, Thirumuruganathan S, Asudeh A, Koudas N, Das G. Efficient construction of approximate ad-hoc ML models through materialization and reuse. Proceedings of the VLDB Endowment. 2018 Jul 1;11(11):1468-81.

Please spread the news and join us for our weekly habit of beginning the week by an interesting machine learning talk!

Subscribe to the mailing list where seminar topics are announced beforehand or check out the next talks at the seminar webpage.


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