This paper describes HyperStream, a large-scale, flexible and robust software package, written in the Python language, for processing streaming data with workflow creation capabilities. HyperStream overcomes the limitations of other computational engines and provides high-level interfaces to execute complex nesting, fusion, and prediction both in online and offline forms in streaming environments. HyperStream is a general purpose tool that is well-suited for the design, development, and deployment of Machine Learning algorithms and predictive models in a wide space of sequential predictive problems. Source code, installation instructions, examples, and documentation can be found at: https://github.com/IRC-SPHERE/HyperStream.
@article{arxiv.1908.02858,
title = {HyperStream: a Workflow Engine for Streaming Data},
author = {Tom Diethe and Meelis Kull and Niall Twomey and Kacper Sokol and Hao Song and Miquel Perello-Nieto and Emma Tonkin and Peter Flach},
journal= {arXiv preprint arXiv:1908.02858},
year = {2019}
}