Prof. Dr. Dan Olteanu , Universität Zürich, Switzerland
Date: Thursday, December 3, 2020, 17:15 h
As we witness the data science revolution, each research community legitimately reflects on its relevance and place in this new landscape. The database research community has at least three reasons to feel empowered by this revolution. This has to do with the pervasiveness of relational data in data science, the widespread need for efficient data processing, and the new processing challenges posed by data science workloads beyond the classical database workloads. The first two aforementioned reasons are widely acknowledged as core to the community’s raison d’être. The third reason explains the longevity of relational database management systems success: Whenever a new promising data-centric technology surfaces, research is under way to show that it can be captured naturally by variations or extensions of the existing relational techniques. Like the Star Trek’s Borg Collective co-opting technology and knowledge of alien species, the Relational Data Borg assimilates ideas and applications from connex fields to adapt to new requirements and become ever more powerful and versatile. Unlike the former, the latter moves fast, has great skin complexion, and is reasonably happy. Resistance is futile in either case.
In this talk, I will make the case for a first-principles approach to machine learning over relational databases that guided recent development in database systems and theory. This includes theoretical development on the algebraic and combinatorial structure of relational data processing. It also includes systems development on compilation for hybrid database and learning workloads and on computation sharing across aggregates in learning-specific batches. Such development can dramatically boost the performance of machine learning.
Dan Olteanu has recently become Professor for Big Data Science at the University of Zurich after spending over 12 years at the University of Oxford. Over the last two decades, he has published in the areas of database systems, database theory, and AI, contributing to XML query processing, incomplete information and probabilistic databases, factorised databases, in-database machine learning, incremental maintenance for analytics, and the commercial systems LogicBlox and relationalAI. He co-authored the book « Probabilistic Databases » (2011). He served or is serving as associate editor for PVLDB (2012, 2020), IEEE TKDE (2013-2015), ACM TODS (2018-), and the SIGMOD Record database principles column (2019-). He also served among others as PC vice chair for SIGMOD 2017 and will serve as PC chair for ICDT 2022. He is the recipient of the ICDT 2019 best paper award, SIGMOD 2018 Distinguished PC member award, an ERC Consolidator grant (2016), and an Oxford Outstanding Teaching award (2009).