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Big data ethics – Ethics of mass data analytics; Big data maturity model – Aspect of computer science; Big memory – A large amount of random-access memory; Data curation – Organization of collected data; Data defined storage – Marketing term for managing data by combining application, information and storage tiers
Data-intensive computing is intended to address this need. Parallel processing approaches can be generally classified as either compute-intensive, or data-intensive. [6] [7] [8] Compute-intensive is used to describe application programs that are compute-bound. Such applications devote most of their execution time to computational requirements ...
Analytics may apply to a variety of fields such as marketing, management, finance, online systems, information security, and software services. Since analytics can require extensive computation (see big data), the algorithms and software used for analytics harness the most current methods in computer science, statistics, and mathematics. [4]
A cloud-based architecture for enabling big data analytics. Data flows from various sources, such as personal computers, laptops, and smart phones, through cloud services for processing and analysis, finally leading to various big data applications. Cloud computing can offer access to large amounts of computational power and storage. [30]
The data from a study can also be analyzed to consider secondary hypotheses inspired by the initial results, or to suggest new studies. A secondary analysis of the data from a planned study uses tools from data analysis, and the process of doing this is mathematical statistics. Data analysis is divided into:
The book received widespread praise for elucidating the consequences of reliance on big data models for structuring socioeconomic resources. Clay Shirky from The New York Times Book Review said "O'Neil does a masterly job explaining the pervasiveness and risks of the algorithms that regulate our lives," while pointing out that "the section on solutions is weaker than the illustration of the ...
Though computational statistics is widely used today, it actually has a relatively short history of acceptance in the statistics community. For the most part, the founders of the field of statistics relied on mathematics and asymptotic approximations in the development of computational statistical methodology.
Tukey defined data analysis in 1961 as: "Procedures for analyzing data, techniques for interpreting the results of such procedures, ways of planning the gathering of data to make its analysis easier, more precise or more accurate, and all the machinery and results of (mathematical) statistics which apply to analyzing data." [3]