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

貪污

書目名稱Big Data Optimization: Recent Developments and Challenges影響因子(影響力)




書目名稱Big Data Optimization: Recent Developments and Challenges影響因子(影響力)學(xué)科排名




書目名稱Big Data Optimization: Recent Developments and Challenges網(wǎng)絡(luò)公開度




書目名稱Big Data Optimization: Recent Developments and Challenges網(wǎng)絡(luò)公開度學(xué)科排名




書目名稱Big Data Optimization: Recent Developments and Challenges被引頻次




書目名稱Big Data Optimization: Recent Developments and Challenges被引頻次學(xué)科排名




書目名稱Big Data Optimization: Recent Developments and Challenges年度引用




書目名稱Big Data Optimization: Recent Developments and Challenges年度引用學(xué)科排名




書目名稱Big Data Optimization: Recent Developments and Challenges讀者反饋




書目名稱Big Data Optimization: Recent Developments and Challenges讀者反饋學(xué)科排名

放縱

Setting Up a Big Data Project: Challenges, Opportunities, Technologies and Optimization, for the enterprise and how value can be derived by analyzing big data. We go on to introduce the characteristics of big data projects and how such projects can be set up, optimized and managed. Two exemplary real word use cases of big data projects are described at the end of the first part. To be

Bernstein-test

Optimizing Intelligent Reduction Techniques for Big Data,le information from data means to combine qualitative and quantitative analysis techniques. One of the main promises of analytics is data reduction with the primary function to support decision-making. The motivation of this chapter comes from the new age of applications (social media, smart cities,

Factorable

Performance Tools for Big Data Optimization, of big data. To accelerate the big data optimization, users typically rely on detailed performance analysis to identify potential performance bottlenecks. However, due to the large scale and high abstraction of existing big data optimization frameworks (e.g., Apache Hadoop MapReduce), it remains a

Dealing

Optimising Big Images,of tens of million data points. Mathematically based models for their improvement—due to noise, camera shake, physical and technical limitations, etc.—are moreover often highly non-smooth and increasingly often non-convex. This creates significant optimisation challenges for the application of the m

斥責(zé)

Interlinking Big Data to Web of Data,s is getting large scale, never ending, and ever changing, arriving in batches at irregular time intervals. Examples of these are social and business data. Linking and analyzing of this potentially connected data is of high and valuable interest. In this context, it will be important to investigate

密碼

nitric-oxide

Applications of Big Data Analytics Tools for Data Management,tworks, wireless communication, and inexpensive memory have all contributed to an explosion of “Big Data”. Our interconnected world of today and the advent of cyber-physical or system of systems (SoS) are also a key source of data accumulation- be it numerical, image, text or texture, etc. SoS is ba

圣人

sultry

Big Data Optimization via Next Generation Data Center Architecture,asingly digital and interconnected world requires both new data analysis algorithms and a new class of systems to handle the dramatic data growth, the demand to integrate structured and unstructured data analytics, and the increasing computing needs of massive-scale analytics. As a result, massive-s