STREAMER is a modern framework that helps scientists to easily integrate and test machine learning algorithms into realistic streaming operational contexts.
Forget about dealing with distributed stream processing engines, STREAMER does it for you!
STREAMER is the first open-source academic framework that is scalable and cross-platform (Linux, Windows, Mac OS).
STREAMER is conceived to be used in two different ways (depending on your necessities):
a) Development use: (oriented to data scientists). You are interested on directly working on the code of the framework to add/develop several functionalities and test them.
b) Product use: (oriented to industrial use). You want to use the framework as a product (no need to get in contact with the code but execute STREAMER).
Experiment with your own use cases or algorithms, and use our API with available algorithms (clustering, classification...), preprocessing functions and evaluation metrics. Integrating your use case is quite simple and can be done in three steps:
(1) Define the data format to be used.
(2) Implement or call the algorithm to be tested.
(3) Configure the streaming context through the property files.