Abstract: 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.
Speaker: Michael Mathioudakis
Affiliation: Professor of Computer Science, University of Helsinki
Place of Seminar: Lecture Hall Exactum D122, University of Helsinki