Research into real-time analytics for big data will enable opportunities to create new businesses and transform existing businesses because it combines elements that form the basis of a step-change in computational performance.
We are currently working on developing data streaming methods that scale to Big Data like large deep neural networks, but work well in all domains.
MOA is the most popular open source framework for data stream mining, with a very active growing community. It includes a collection of machine learning algorithms (classification, regression, clustering, outlier detection, concept drift detection and recommender systems) and tools for evaluation, that are suitable for data streams, i.e. cases where one doesn’t have the opportunity to re-process the data multiple times.
- Albert Bifet
- Heitor Murilo Gomes
- Bernhard Pfahringer
- Geoff Holmes
Heitor Murilo Gomes, Albert Bifet, Jesse Read, Jean Paul Barddal, Fabrício Enembreck, Bernhard Pfharinger, Geoff Holmes, Talel Abdessalem:
Adaptive random forests for evolving data stream classification.Machine Learning 106(9-10): 1469-1495 (2017)