To deal with visualization challenges above identified, the issues of massive datasets, continuous unbounded streams and
learned models, PROTEUS will develop innovative solutions based on incremental visual methods that allow end-users to explore both data-at-rest and data-in-motionefficiently to make well-informed decisions in real time. PROTEUS will also
investigate new visualisation paradigms dedicated to Big Data (both at-rest and in-motion) that help inspect the data as well to explain the behaviour of the algorithms.
The incremental visual approach that PROTEUS will implement consists of three main layers: Data Collector, Incremental Analytics Engine and Visualization Layer. The
Data Collector is in charge of iteratively getting new data from data sources (both static and streaming) and sending them to the second layer. The
Incremental Analytics Engine processes data using the online machine learning algorithms and outputs partial results which will be visualized by the
third layer. The visualization of the intermediate results at various iterations allows the users to track and interact with those results in real time.