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The phrase "massive datasets" is correct and commonly used in written English.
It is typically used to refer to large collections of data that require specialized tools and techniques for analysis. Example: The researchers used complex algorithms to process the massive datasets and uncover patterns and trends in consumer behavior.
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Mining Massive Datasets Course, Stanford, 2016.
In winter 2013 I taught CS246: Mining Massive Datasets.
In winter 2011 I taught CS246: Mining Massive Datasets.
Availability of massive datasets is revolutionizing science and industry.
In winter 2012 I taught CS246: Mining Massive Datasets.
Network theory has become the standard methodology to frame, develop and analyze such massive datasets.
New cohorts and studies have produced massive datasets consisting of over 100,000 individuals.
His main research interests lie in algorithms for massive datasets and sub-linear time streaming algorithms.
Scaling up Artificial Intelligence (AI) algorithms for massive datasets to improve their performance is becoming crucial.
Massive datasets arise in a broad spectrum of scientific, engineering and commercial applications.
Big data represents a new scale and complexity that might be achieved by combining massive datasets and analysing them to extract insight for charities and social purpose organisations.
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