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Open standards in processor innovation with RISC-V

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Big data applications that analyze very large and disparate datasets using computations and algorithms are spawning. These applications reveal trends, patterns, and associations. These valuable insights connect and drive more precise predictions and enable better decisions to achieve better outcomes. Because big data analysis is based on information captured from the past, today's applications also require immediate analysis of information as it happens.

As a result, there's a parallel track accompanying big data: fast data, where the immediacy of data is critical. Fast data has a different set of characteristics. Fast data applications process or transform data as it is captured, leveraging the algorithms derived from big data to provide real-time decisions and results. Whereas big data provides insights derived from "what happened" to forecast "what will likely happen" (predictive analysis), fast data delivers insights that drive real-time actions. This is particularly beneficial to "smart" machines, environmental monitors, security and surveillance systems, securities trading systems, and applications that require analysis, answers, and actions in real time.

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