Enhancing Selectivity in Big Data

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.

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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.