Visual Control of Big Data - MIT Very interesting new development.
" ... The Database Group at MIT’s Computer Science and Artificial Intelligence Laboratory has released a data-visualization tool that lets users highlight aberrations and possible patterns in the graphical display; the tool then automatically determines which data sources are responsible for which. ...
It could be, for instance, that just a couple of faulty sensors among dozens are corrupting a very regular pattern of readings, or that a few underperforming agents are dragging down a company’s sales figures, or that a clogged vent in a hospital is dramatically increasing a few patients’ risk of infection. ...
... The idea of provenance tracking is not new, but Wu’s system is particularly well suited to the task of tracking down outliers in data visualizations. Rather than simply telling the user the million data entries that were used to compute the outliers, it first identifies those that most influenced the outlier values, and summarizes those data entries in human readable terms. ... "
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