In the age of big data, visualization tools are vital. With a single glance at a graphic display, a human can recognize patterns that a computer might fail to find even after hours of analysis.

But what if there are aberrations in the patterns? Or what if there’s just a suggestion of a visual pattern that’s not distinct enough to justify any strong inferences? Or what if the pattern is clear, but not what was expected?

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 each aberration or pattern.

It could be, for instance, that just a couple of faulty sensors among dozens are corrupting an otherwise 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.

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