Titlebook: Big Data Optimization: Recent Developments and Challenges; Ali Emrouznejad Book 2016 Springer International Publishing Switzerland 2016 Bi

拥护者

红润

Smart Sampling and Optimal Dimensionality Reduction of Big Data Using Compressed Sensing,omputational biology and social networks are now overwhelmed with large-scale databases that need computationally demanding manipulation. Several techniques have been proposed for dealing with big data processing challenges including computational efficient implementations, like parallel and distrib

Spinal-Fusion

越自我

修正案

Big Network Analytics Based on Nonconvex Optimization,ssing. Many network issues can be modeled as nonconvex optimization problems and consequently they can be addressed by optimization techniques. In the pipeline of nonconvex optimization techniques, evolutionary computation gives an outlet to handle these problems efficiently. Because, network commun

DUST

Large-Scale and Big Optimization Based on Hadoop,nal issues in modeling real-world problems. Computation can easily outgrow the computing power of standalone computers as the size of problem increases. The modern distributed computing releases the computing power constraints by providing scalable computing resources to match application needs, whi

LUMEN

Simulate

性冷淡

Kaleidoscope

Big Data: Who, What and Where? Social, Cognitive and Journals Map of Big Data Publications with Foclarge data handling different scientific communities are working. We employ scientometric mapping techniques to identify who works on what in the area of big data and large scale optimization problems.