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دسته بندی:
داده های بزرگ - big data
سال انتشار:
2016
عنوان انگلیسی مقاله:
Nomadic Computing for Big Data Analytics
ترجمه فارسی عنوان مقاله:
محاسبات Nomadic برای تحلیل داده های بزرگ
منبع:
IEEE - Computer ( Volume: 49, Issue: 4, Apr: 2016 )
نویسنده:
Hsiang-Fu Yu,Cho-Jui Hsieh,Hyokun Yun,S.V.N. Vishwanathan, Inderjit Dhillon,
چکیده انگلیسی:
oday’s applications often contain datasets that are too big to fit in a single comput er’s main memory. Analyzing these massive datasets will require scalable and sophisti- -cated machine-learning methods. Two commonly used approaches are stochastic optimization and inference
algorithms,1 which process one data point at a time; and distributed computing based on the MapReduce framework,2 where the computation proceeds in iterations, with a master processor distributing the computation to slaves at each iteration. Although stochastic optimization and inference algorithms are effective for largescale machine learning, they are inherently sequential.On the other hand, MapReduce-based algorithms suffer from the curse of the last reducer, in that the slaves must wait for the slowest processor to finish before moving on to the next computational iteration.
Keywords: Stochastic processes | Charge coupled devices | Scalability | Big data | Motion pictures | Inference algorithms | Partitioning algorithms
قیمت: رایگان
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