Enhancing Selectivity in Big Data

By Lori Cameron
Published 03/30/2018
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Today’s companies collect immense amounts of personal data and enable wide access to it within the company. This exposes the data to external hackers and privacy-transgressing employees, say the authors of “Enhancing Selectivity in Big Data.” (Login may be required for full text.)

Researchers from Microsoft, Uber, and Columbia University show that, for a wide and important class of workloads, only a fraction of the data is needed to approach state-of-the-art accuracy.

They propose selective data systems that are designed to pinpoint the data that is valuable for a company’s current and evolving workloads. These systems limit data exposure by setting aside the data that is not truly valuable.



About Lori Cameron

Lori Cameron is a Senior Writer for the IEEE Computer Society and currently writes regular features for Computer magazine, Computing Edge, and the Computing Now and Magazine Roundup websites. Contact her at l.cameron@computer.org. Follow her on LinkedIn.