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Hydroclimatic research requires highly intensive resources of big data and computation to perform simulations.
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In the future, we expect to update the data and improve its performance by adopting data compression and multi-threading computation techniques demanded by big data and high-speed processing when larger number of K-series of additional genomes are included.
For the computation time, there is no doubt at all that parallel computing is one of the important future trends to make the data analytics work for big data, and consequently the technologies of cloud computing, Hadoop, and map-reduce will play the important roles for the big data analytics.
MapReduce [3, 4] is a programming model for implementation of distributed computations of high volume or big data and an execution framework for large-scale data processing on clusters of servers.
We define "Big Networks" as those that generate big data and can benefit from big data management in their operations.
The recent explosive publications of big data studies have well documented the rise of big data and its ongoing prevalence.
Our research suggests that the combination of big data and classical management models can bring success for big data commerce.
"Big data" and "sex trafficking".
However, with the rise of big data force, data and computation tend to be centralized.
Some big data storage, computation models, and their architecture are introduced.
Big data is providing computation and efficient processing to millions of users which has same complexity level just like CAS (Habiba et al. 2014).
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