In a lecture at the 9th Annual Massachusetts Institute of Technology (MIT) Chief Data Officer & Information Quality Symposium, 2014 ACM A.M. Turing Award recipient Michael Stonebraker discussed how the future of big data usage hinges on several factors.
One factor, which he and his collaborators are attempting to address, is the need to exploit falling computer memory costs to store data longer and retrieve it at faster speeds.
Another key challenge Stonebraker's team is concentrating on solving is what he calls "big analytics on big volumes of data." Stonebraker says the growing need for running complex analytics on the increasing volumes of data leads to an "array database" solution supporting sophisticated statistical procedures that are beyond the capabilities of table-based, traditional databases.
Stonebraker also foresees data integration as a future big data trend, which he terms the "Big Variety" problem. He argues the Extract-Transform-Load data integration problem lacks scalability, and he founded the startup Tamr in 2013 to develop a combined solution of automated machine learning and crowdsourcing of domain experts.
Stonebraker and colleagues devised a method for masking data silos by superimposing on them a software layer that adjusts to the constantly changing semantic environment of the organization, based on a human-computer collaborative process.
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Abstracts Copyright © 2015 Information Inc., Bethesda, Maryland, USA
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