Peter Triantafillou

Towards Learned Data Management

Machine Learning (ML) is revolutionizing data management. Fundamentally, as many internal DB components rest essentially on a prediction function, ML offers the promise of improving functionality and performance. Examples of applications of ML algorithms, models, and principles for improving DB functionality and performance abound. In this talk our recent research on three key areas will be overviewed.

Specifically, we will discuss: First, how to best adapt specific deep learning networks for fast and accurate Approximate Query Processing. Second, how adapting principles from Probabilistic Graphical Models can lead to new ways to perform physical joins, and/or analytical queries over joins, and/or facilitating downstream analytics over joins in a manner that significantly outperform the state of the art in terms of time, space, and scalability. Finally, a general framework will be presented that deals effectively with the problems faced by learned DB components/models in the presence of new data following different (to the learned) distributions. The proposed framework can handle different types of neural networks, trained for a variety of different learning tasks (e.g. AQP, selectivity estimation, synthetic data generation/sampling).

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Peter Triantafillou is Professor of Data Systems at the Department of Computer Science at the University of Warwick where he established and is currently leading the eight-faculty strong Data Sciences research theme. Peter is currently a Fellow of the Alan Turing Institute, a member of the Advisory Board of PVLDB, PC co-Chair of PVLDB Reproducibility, and Associate Editor for ACM SIGMOD 2022. Peter has served as a member of the Advisory Board of the Huawei Ireland Research Centre and as a member of the Advisory Board of the Urban Big Data Research Centre (a UK national infrastructure for urban data services and analytics). Peter received his PhD in computer science from the University of Waterloo and was the Department of Computer Science and the Faculty of Mathematics nominee for the Gold Medal for outstanding achievements at the Doctoral level. Peter has published extensively in top journals and conferences in his areas and his papers have won numerous awards, including the most influential paper award in ACM DEBS 2019, the best paper award at the ACM SIGIR 2016 Conference, the best paper award at the ACM CIKM Conference 2006, and the best student paper award at IEEE Big Data 2018 Conference. Peter has served in the Technical Program Committees of more than 140 international conferences and has been the PC Chair or Vice-chair/Associate Editor in several prestigious conferences.