The ultimate goal of any visual analytic task is to make sense of the data and gain insights. Unfortunately, the continuously growing scale of the data nowadays challenges the traditional data analytics in the “big-data” era. Particularly, the human cognitive capabilities
are constant whereas the data scale is not. Furthermore, most existing work focus on how to extract interesting information and present that to the user while not emphasizing on how to provide options to the analysts if the extracted information is not interesting. In this paper, we propose
a visual analytic tool called MaVis that integrates multiple machine learning models with a plug-and-play style to describe the input data. It allows the analysts to choose the way they prefer to summarize the data. The MaVis framework provides multiple linked analytic spaces for interpretation
at different levels. The low level data space handles data binning strategy while the high level model space handles model summarizations (i.e. clusters or trends). MaVis also supports model analytics that visualize the summarized patterns and compare and contrast them. This framework is shown
to provide several novel methods of investigating co-movement patterns of timeseries dataset which is a common interest of medical sciences, finance, business and engineering alike. Lastly we demonstrate the usefulness of our framework via case study and user study using a stock price dataset.
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Document Type: Research Article
February 14, 2016
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