Authors:
Jeffrey Kim, Arnie Lund, Caroline Dombrowski
Since as far back as at least 1995, researchers have worried about how to analyze ever-growing, overwhelming, gargantuan data. For much of big data research and analysis, computational feasibility is no longer the challenge. Peter Huber's taxonomy of dataset sizes classified "ridiculous" as 10 to the 12th power number of bytes, with storage on "robotic magnetic tape" [1]. Times have changed. Complex computations do require much more processing power, but now the problem more often is that little is being done with big data, or only rudimentary, surface-level analysis—which is a shame, because larger, more complex datasets are also,…
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