Real-Time Machine Learning Competition on Data Streams at the IEEE Big Data 2019

In this paper, we present the competition "Real-time Machine Learning Competition on Data Streams", a BigData Cup Challenge of the IEEE Big Data 2019 conference. Data streams, such as data originated from sensors, have increasingly gained the interest of researchers and companies and are currently widely studied in data science. Companies in the telecommunication and energy industries are trying to exploit these data and get real-time insights on their services and equipment. In order to extract valuable knowledge from data streams, one must be able to analyze the data as they arrive and make meaningful predictions. For this purpose, we use fast incremental learners. There already exists a great community that is organizing various competitions on machine learning tasks for batch learners. Our goal was to introduce the same approach to engage the whole community in solving essential problems in data stream mining. We performed a new kind of data science competition based on a real-time prediction setting, using a novel competition platform on data streams. The examples to predict were released in real-time, and the predictions had also to be submitted in real-lime. To the best of our knowledge, this was the first data science competition conducted in real-lime. The task of the competition was to predict network activity, and the data has been provided by one of our partner companies.