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Less scatterbrained scatterplots

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Less scatterbrained scatterplots

“In these scatterplots, you are able to see overall trends and outliers, but the overplotting and the static nature of the plot limit the user’s ability to interact with the chart,” says Tao.
In contrast, Kyrix-S can produce a version (below) that puts data in several zoom levels, enabling interaction with each county. To avoid overplotting, Kyrix-S’ scatterplot also shows only the most important examples, like the most populous counties.
Kyrix-S is currently being used by Data Civilizer 2.0, a data integration platform developed at MIT. An earlier version was also employed to help Massachusetts General Hospital analyze a massive brain activity dataset (EEG) that clocks in at 30 terabytes — the equivalent of more than 50,000 hours of digital music. (The goal of that study was to train a model that predicts seizures, given a series of 2-second EEG segments.)
Moving forward, the researchers will be adapting Kyrix-S to work as part of a graphical user interface. They also plan to add functionality so that the system can handle data that is being continuously updated.
Tao wrote a paper about Kyrix-S alongside MIT Adjunct Professor Mike Stonebraker, researchers Xinli Hou and Adam Sah, Leilani Battle SM ’13, PhD ’17, and Professor Remco Chang of Tufts University. It will be presented virtually at IEEE’s VIS data visualization conference Oct. 25.

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