29–30 Oct 2018
Hotel Mercure Budapest
Europe/Budapest timezone

Online Machine Learning in Big Data Streams - Theory and Practice

29 Oct 2018, 15:35
25m
Mátyás Hall (Groundfloor) (Hotel Mercure Budapest)

Mátyás Hall (Groundfloor)

Hotel Mercure Budapest

Krisztina körút 41-43. 1013 Budapest Hungary
Lecture

Speaker

Andras Benczur (Institute for Computer Science and Control, Hungarian Academy of Sciences)

Description

The area of online machine learning in big data streams covers algorithms that are (1) distributed and (2) work from data streams with only a limited possibility to store past data. The first requirement mostly concerns software architectures and efficient algorithms. The second one also imposes nontrivial theoretical restrictions on the modeling methods: In the data stream model, older data is no longer available to revise earlier suboptimal modeling decisions as the fresh data arrives.

In my presentation, I will provide an overview of distributed software architectures and libraries as well as machine learning models for online learning. I will highlight the most important ideas for classification, regression, recommendation, and unsupervised modeling from streaming data, and we show how they are implemented in various distributed data stream processing systems. I will also explore the usability of online machine learning, especially for recommender systems and industrial IoT applications.

Primary author

Andras Benczur (Institute for Computer Science and Control, Hungarian Academy of Sciences)

Co-authors

Robert Palovics (Stanford University) Levente Kocsis (Institute for Computer Science and Control of the Hungarian Academy of Sciences)

Presentation materials