ViA-ML: A Machine Learning backed Visualization Assistant

With the proliferation of cheap sensors, reduction in storage costs and the ubiquity of communication networks, Cyber-Physical Systems are collecting and storing data at an unprecedented rate. Analysis of such large databases is necessary to find relevant information and improve the efficiency of the Cyber Physical System. The goal of an analysis tool, simply put is to find the most interesting information in the data and present it to the user in most intuitive and clear manner possible by effectively mapping the information to visual cues. In response to this need, SSCI is proposing the development of ViA-ML, a visualization assistant with an interest-driven machine learning back-end to allow users to interactively extract information from large datasets collected by cyber physical systems. Our proposed approach is based on providing analysts with visualizations that maximize view comprehension, using pyschophysics based criteria, of the raw data attributes and attributes derived from automated analysis. Maximizing viewer comprehension then allows us to quickly gauge user interest, iterate through competing hypotheses by our novel machine learning algorithms and further enhance the visualizations by incorporating user knowledge and requirements.