Big data is a term for data sets that are so large or complex that traditional data processing application software is inadequate to deal with them. Challenges include capture, storage, analysis, data curation, search, sharing, transfer, visualization, querying, updating and information privacy. The term “big data” often refers simply to the use of predictive analytics, user behavior analytics, or certain other advanced data analytics methods that extract value from data, and seldom to a particular size of data set. Scientists, business executives, practitioners of medicine, advertising and governments alike regularly meet difficulties with large data-sets in areas including Internet search, finance, urban informatics, and business informatics. Scientists encounter limitations in e-Science work, including meteorology, complex physics simulations, biology and environmental research. Data sets grow rapidly – in part because they are increasingly gathered by cheap and numerous information-sensing mobile devices, aerial, software logs, cameras, microphones, radio-frequency identification readers and wireless sensor networks. Relational database management systems, desktop statistics, and visualization-packages often have difficulty handling big data. What counts as “big data” varies depending on the capabilities of the users and their tools, and expanding capabilities make big data a moving target.
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