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Complexity of the data poses a challenge.
Automatic indexing and retrieval of digital data poses major challenges.
Nevertheless, the high-dimensional sequencing data poses a great challenge for statistical analysis.
Nonetheless, modeling such data poses new challenges related to data volume, diversity, inhomogeneity and the required granularity level.
Inference of reservoir flow properties from scattered production data poses a poorly-constrained inverse problem with non-unique solutions.
Bulk and indiscriminate collection of data poses a serious and severe threat to our civil liberties, including our rights to free expression and to privacy".
However, using clinical trials for collecting economic data poses several challenges, and the methods for conducting such evaluations are being developed.
Preparation of this data poses a unique engineering challenge because of the sheer volume of the external-memory data sets involved.
While hyperspectral data are rich in information, processing the hyperspectral data poses several challenges regarding computation speed requirements, information redundancy removal, relevant information identification, and modeling accuracy.
A new domain of machine learning, in which the prediction must satisfy the additional constraints found in structured data, poses one of machine learning's greatest challenges: learning functional dependencies between arbitrary input and output domains.
Professor Omer Tene, privacy expert in law and technology, sat down with TAP to discuss the risks big data poses to privacy and the challenges to existing privacy rules.
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CEO of Professional Science Editing for Scientists @ prosciediting.com