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Unscalability and centralization Most data analysis methods are not for large-scale and complex dataset.
According to our observation, most data analysis methods have limitations for big data, that can be described as follows: Unscalability and centralization Most data analysis methods are not for large-scale and complex dataset.
The most data analysis methods have used dynamic analysis with 51%, the hybrid analysis has 29% and the static analysis has 20% usage.
Since most machine learning algorithms can be used to find an approximate solution for the optimization problem, they can be employed for most data analysis problems if the data analysis problems can be formulated as an optimization problem.
The experiments were performed in fresh water (Coralville Lake, IA) and salt water (Panama City Beach, FL), at varying velocities with most data analysis performed at 10, 14 and 18 knots.
Most data analysis was performed with Spectrum [18] (version 3.3), a demographic projection software with a built-in HIV/AIDS module; some Spectrum output data were further analyzed using Microsoft Excel.
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However, Google has the advantage of the most massive data analysis and most seriously challenging (i.e. revolutionary) business model on the planet--enough so that they literally may have the ability to know exactly what we want, as we want it, and be able to deliver it the way we need it.
The pair began to investigate the inner workings of the oncology industry and realized that most cancer care centers, oncologists and researchers were deprived of even the most basic data analysis tools that other industries used daily.
The effects of even the most advanced data analysis techniques are not huge.
In November, Google offered a limited number of developers access to some of its most powerful data analysis software, part of what Google uses to index the Internet, in a product called BigQuery.
Most existing data analysis tools (in particular SPSS) are built for statisticians.
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