Project description


PROTEUS is presented as three key technology components (hybrid computation model for both data-at-rest and data-in-motion, scalable online machine learning and real-time interactive visual analytics) integrated into the existing Apache Flink, the EU Big Data platform, to provide a validated solution for specific problems in an industrial setting related to steelmaking. However the project contributions will be generic and context-independent to be applied in all data stream driven domains.


The core innovations and value of PROTEUS are based on a new integrated processing engine for being able to apply complex analytics techniques at scale and for batch data (data-at-rest) and data streams (data-in-motion) in a hybrid-merge mode. In this way, our predictive engine will be able to provide real-time predictions while self-adapts continuously to learn more complex and refined learning models. Moreover, visual analytics will be scalable with decreasing latency (interactive) demands using a novel incremental approach that represents the information (both data-in-motion and incremental process of batch data) as data streams.