Titlebook: High-Performance Big-Data Analytics; Computing Systems an Pethuru Raj,Anupama Raman,Siddhartha Duggirala Book 2015 Springer International P

ODDS

书目名称High-Performance Big-Data Analytics影响因子(影响力)




书目名称High-Performance Big-Data Analytics影响因子(影响力)学科排名




书目名称High-Performance Big-Data Analytics网络公开度




书目名称High-Performance Big-Data Analytics网络公开度学科排名




书目名称High-Performance Big-Data Analytics被引频次




书目名称High-Performance Big-Data Analytics被引频次学科排名




书目名称High-Performance Big-Data Analytics年度引用




书目名称High-Performance Big-Data Analytics年度引用学科排名




书目名称High-Performance Big-Data Analytics读者反馈




书目名称High-Performance Big-Data Analytics读者反馈学科排名

contradict

音的强弱

1617-7975 ase studies and examples of best practice.Includes learning This book presents a detailed review of high-performance computing infrastructures for next-generation big data and fast data analytics. Features: includes case studies and learning activities throughout the book and self-study exercises in

大包裹

In-Database Processing and In-Memory Analytics,rocessing to the data is the principle advocated by in-database processing, while the in memory focuses on keeping the data completely in memory to increase the processing speed. In this chapter, we will learn and analyze these two and study some use case to improve our understanding.

凹处

High-Performance Grids and Clusters,rids is vastly different. The clusters are generally employed with the locally interconnected systems, whereas grids are employed at a much wider and distributed scale. In this chapter we will learn more about these two interconnected paradigms and study some use cases.

繁重

Incorruptible

Spina-Bifida

High-Performance Computing (HPC) Paradigms, extinct but have surprised the business on how it has evolved with better capabilities to handle real-time data and the so-called big data. This chapter covers all interesting solutions on how mainframe can still be used to provide better client solutions.

光亮

High-Performance Peer-to-Peer Systems,jor theme for high-performance computing. With goals of dynamism, ad hoc collaboration, and cost sharing, these platforms show the following distinguishing traits: decentralization, highly scalability, and low cost of ownership. In this chapter, we learn about the design goals and principles and various commercial and scientific systems available.

cajole

Big Data Analytics for Healthcare,nts. In this chapter, we will talk about various market factors affecting healthcare and how big data and analytics can help bring out value from data. We will look into interesting facts about various technology adoptions like IBM Watson and how these new technology adoptions play a vital role in improving the quality of care.