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There has always been a close relationship between big data and cloud computing as it requires no limits in terms of compute and storage but by adding real-time processing capabilities, we will see a rise in data analytics that are able to produce results for in real-time, radically changing the products companies can build.
Investigating the correlation between big data and smart city applications.
In this case the initial error was a marriage between big data and small data.
2, we examine the conflict between big data and the legal and ethical requirements in private data management.
In this paper, a social collaborative Internet of Things (IoT) based smart flood monitoring and forecasting architecture is proposed with the convergence between big data and HPC.
While it is obvious that there are some tensions between big data and data anonymization, we should not rule out the latter.
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That includes trying to get everyone to understand the difference between big data, open data and personal data.
For communication with other system, the security problem is on the communications between big data analytics and other external systems.
Using a translog function, Bughin [7] for instance highlights strong complementarity between big data labor and big data specific capital investments.
k > 0 would confirm the existence of complements between big data IT and talent resources; m > 0, would be a proof of returns to diversity, and n < 0 implies organizational misfit.
Log(LD) measure big data capital and labour elasticities; The sign of β2 as well of δ2 measures growing (if positive) or declining (if negative) returns in new big data investments in capital and labour input; The sign of γ, measures the extent of substitution, (if negative) or of complementary (if positive) between big data labour and capital.
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between big countries and
between biometric data and
between anonymous data and
between big steelmakers and
between big corporations and
between official data and
between big insurers and
between big media and
between economic data and
between big venues and
between big companies and
between useful data and
between big carriers and
between big cities and
between rich data and
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