Name
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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Year | DOI | Venue |
|---|---|---|
2020 | 10.1145/3340531.3417427 | CIKM '20: The 29th ACM International Conference on Information and Knowledge Management
Virtual Event
Ireland
October, 2020 |
DocType | ISBN | Citations |
Conference | 978-1-4503-6859-9 | 0 |
PageRank | References | Authors |
0.34 | 0 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Sandra Garcia-Rodriguez | 1 | 0 | 0.34 |
| Mohammad Alshaer | 2 | 0 | 0.34 |
| Cédric Gouy-Pailler | 3 | 62 | 10.69 |