Online Machine Learning in Big Data Streams

02/16/2018
by   András A. Benczúr, et al.
0

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 this article, we provide an overview of distributed software architectures and libraries as well as machine learning models for online learning. We 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. This article is a reference material and not a survey. We do not attempt to be comprehensive in describing all existing methods and solutions; rather, we give pointers to the most important resources in the field. All related sub-fields, online algorithms, online learning, and distributed data processing are hugely dominant in current research and development with conceptually new research results and software components emerging at the time of writing. In this article, we refer to several survey results, both for distributed data processing and for online machine learning. Compared to past surveys, our article is different because we discuss recommender systems in extended detail.

READ FULL TEXT

page 1

page 2

page 3

page 4

research
05/26/2018

Large-Scale Learning from Data Streams with Apache SAMOA

Apache SAMOA (Scalable Advanced Massive Online Analysis) is an open-sour...
research
06/29/2022

Signature Methods in Machine Learning

Signature-based techniques give mathematical insight into the interactio...
research
11/18/2022

TensAIR: Online Learning from Data Streams via Asynchronous Iterative Routing

Online learning (OL) from data streams is an emerging area of research t...
research
10/29/2021

Parallel-and-stream accelerator for computationally fast supervised learning

Two dominant distributed computing strategies have emerged to overcome t...
research
08/21/2022

A Survey on Transactional Stream Processing

Transactional stream processing (TSP) has been increasingly gaining trac...
research
05/15/2020

Intelligent Tutoring Systems for Generation Z’s Addiction

As generation Z’s big data is flooding the Internet through social nets,...
research
04/15/2020

Intelligent Tutoring Systems for Generation Z's Addiction

As generation Z's big data is flooding the Internet through social nets,...

Please sign up or login with your details

Forgot password? Click here to reset