Keeping Pace with Extreme Data
نویسنده
چکیده
A fter perusing the articles and guest editors’ introduction for this issue on extreme data, I googled “extreme,” and hundreds of links bubbled up—from computing, coupons, data, and fatigue, to science, scientists and science fun, to “extreme x” (don’t ask). I then concluded that “extreme” has become an unreliable buzzword, used somewhat indiscriminately for its shock value. That said, the fact remains that the problems of dealing with very large sets of data do plague us scientists, despite emerging new technologies. And still, our models, sensing devices, telescopes, and even the Internet, threaten to bury us ever deeper in data. So, maybe “extreme data” is a relevant term for us to use. New technology is certainly necessary, but is it sufficient? As the guest editors Manish Parashar and George Thiruvathukal point out in their introduction to this special issue (see p. 8), “growing data volumes, expanding distribution and dynamism of the data, [and] ... growing costs ... associated with transporting and processing this data, require new paradigms and practices in data management and analytics.” What’s the conscientious scientist to do? How can she keep up with both her own field and the dynamic science of data management, applying new (and as yet unproven) technologies to her own work? More fundamentally, to what extent do “paradigms and practices” include more than technology? How does technological innovation—not directly related to the scientific question at hand—come about in the sciences?
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ورودعنوان ژورنال:
- Computing in Science and Engineering
دوره 16 شماره
صفحات -
تاریخ انتشار 2014