19/10/2020

STREAMER: A powerful framework for continuous learning in data streams

Sandra Garcia-Rodriguez, Mohammad Alshaer, Cedric Gouy-Pailler

Keywords: realtime analysis, ecg, streaming framework, machine learning, data stream

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Abstract: With the proliferation of continuous data generation, data stream processing has become a key topic in research. As a consequence, the need for dedicated tools to apply continuous learning in streams emerges. This paper presents STREAMER, a flexible, scalable, and cross-platform machine learning experimenter with a realistic operational stream environment and visualization capabilities. Oriented to data scientists, this framework provides a set of machine learning algorithms and an API to easily integrate new ones. In order to illustrate how STREAMER works, we show a demonstration of an unsupervised anomaly detection of electrocardiograms (ECG) tested in a streaming context.

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