Download the whitepaper: A Benchmark Study of Large-Scale Chemical Classification using ParallelR
The world is awash with digital data online. Utilizing this data to yield knowledge is the big challenge. The raw data by itself is rather worthless. Modern data mining techniques have emerged as a potential solution, but they are sufficiently compute intensive for real world applications that conventional PCs and servers often cannot provide knowledge to us in a timely fashion. This is a major issue as CPU clock rates seem to have leveled off and data sets (and subsequent run times) are increasing exponentially.
In this paper, we will show that by utilizing an “off the shelf” parallel data mining R package called "caretNWS", knowledge workers can use quad-core processor-based systems to classify data with a minimum of effort and yet realize high performance. For the first time, knowledge workers can achieve scalable data mining without resorting to parallel programming.
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