標(biāo)題: Titlebook: Big Data Optimization: Recent Developments and Challenges; Ali Emrouznejad Book 2016 Springer International Publishing Switzerland 2016 Bi [打印本頁] 作者: 貪污 時(shí)間: 2025-3-21 18:22
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書目名稱Big Data Optimization: Recent Developments and Challenges影響因子(影響力)學(xué)科排名
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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
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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
作者: 放縱 時(shí)間: 2025-3-21 22:41
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 作者: Bernstein-test 時(shí)間: 2025-3-22 02:57
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,作者: Factorable 時(shí)間: 2025-3-22 06:54
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 作者: Dealing 時(shí)間: 2025-3-22 12:05
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作者: 斥責(zé) 時(shí)間: 2025-3-22 16:36
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 作者: 密碼 時(shí)間: 2025-3-22 20:18 作者: nitric-oxide 時(shí)間: 2025-3-23 00:00
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作者: 圣人 時(shí)間: 2025-3-23 01:43 作者: sultry 時(shí)間: 2025-3-23 06:07
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作者: 擁護(hù)者 時(shí)間: 2025-3-23 11:08 作者: 紅潤(rùn) 時(shí)間: 2025-3-23 15:39
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作者: Spinal-Fusion 時(shí)間: 2025-3-23 18:05 作者: 越自我 時(shí)間: 2025-3-24 00:33 作者: 修正案 時(shí)間: 2025-3-24 05:33
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作者: DUST 時(shí)間: 2025-3-24 09:41
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作者: LUMEN 時(shí)間: 2025-3-24 12:33 作者: Simulate 時(shí)間: 2025-3-24 16:37 作者: 性冷淡 時(shí)間: 2025-3-24 20:44 作者: Kaleidoscope 時(shí)間: 2025-3-24 23:37
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.作者: DAMN 時(shí)間: 2025-3-25 07:17
Big Data Optimization Within Real World Monitoring Constraints, by allowing these systems to comply to real world constrains in the area of performance, reliability and reliability. Using several examples of real world monitoring systems this chapter discusses different approaches in optimization: data, analysis, system architecture and goal oriented optimization.作者: Euphonious 時(shí)間: 2025-3-25 09:20 作者: Ablation 時(shí)間: 2025-3-25 11:43 作者: APRON 時(shí)間: 2025-3-25 18:56
Patrice Degoulet,Marius Fieschil performance tools for big data optimization from various aspects, including the requirements of ideal performance tools, the challenges of performance tools, and state-of-the-art performance tool examples.作者: Femish 時(shí)間: 2025-3-25 23:19
Performance Tools for Big Data Optimization,l performance tools for big data optimization from various aspects, including the requirements of ideal performance tools, the challenges of performance tools, and state-of-the-art performance tool examples.作者: Conduit 時(shí)間: 2025-3-26 02:47
Susannah Orzell,Amar Suryadevaralarge 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.作者: Rheumatologist 時(shí)間: 2025-3-26 05:18 作者: Seizure 時(shí)間: 2025-3-26 09:12
2197-6503 ne book.Presents useful big data optimization applications i.The main objective of this book is to provide the necessary background to work with big data by introducing some novel optimization algorithms and codes capable of working in the big data setting as well as introducing some applications in作者: 反饋 時(shí)間: 2025-3-26 16:28 作者: 有說服力 時(shí)間: 2025-3-26 18:49 作者: 狗舍 時(shí)間: 2025-3-26 22:23 作者: bacteria 時(shí)間: 2025-3-27 03:03 作者: Accolade 時(shí)間: 2025-3-27 05:24 作者: 持續(xù) 時(shí)間: 2025-3-27 10:32 作者: rods366 時(shí)間: 2025-3-27 17:09 作者: 多骨 時(shí)間: 2025-3-27 21:06
Ali EmrouznejadPresents recent developments and challenges in big data optimization.Collects various recent algorithms in large-scale optimization all in one book.Presents useful big data optimization applications i作者: STALL 時(shí)間: 2025-3-28 01:48 作者: originality 時(shí)間: 2025-3-28 04:57
Susannah Orzell,Amar Suryadevara large amounts of data. These large amounts of data present various challenges, one of the most intriguing of which deals with knowledge discovery and large-scale data-mining. This chapter investigates the research areas that are the most influenced by big data availability, and on which aspects of 作者: prostate-gland 時(shí)間: 2025-3-28 09:08 作者: 依法逮捕 時(shí)間: 2025-3-28 14:20
Prateek D. Wali,Manika Suryadevarale 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,作者: Urea508 時(shí)間: 2025-3-28 16:23 作者: Oratory 時(shí)間: 2025-3-28 22:03
Introduction to Clinical Informaticsof 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作者: CLIFF 時(shí)間: 2025-3-29 00:55
Arthur N. Wiens,Angelique G. Tindalls 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 作者: Myocarditis 時(shí)間: 2025-3-29 05:21
Introduction to Clinical Psychologys, and Principal component analysis. The related area of Topological Data Analysis (TDA) has been developing in the last decade. The idea is to extract robust topological features from data and use these summaries for modeling the data. A topological summary generates a coordinate-free, deformation 作者: upstart 時(shí)間: 2025-3-29 09:40 作者: HAIL 時(shí)間: 2025-3-29 15:10 作者: 顛簸下上 時(shí)間: 2025-3-29 18:43
Robert L. Hale,Elizabeth A. Greenasingly 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作者: FLAG 時(shí)間: 2025-3-29 21:54
Arthur N. Wiens,Angelique G. Tindalle interpretation also grows. To this, as decision makers require the timely arrival of information, the need for high performance interpretation of measurement data also grows. Big Data optimization techniques can enable designers and engineers to realize large scale monitoring systems in real life,作者: 重畫只能放棄 時(shí)間: 2025-3-30 00:24 作者: 官僚統(tǒng)治 時(shí)間: 2025-3-30 06:04 作者: champaign 時(shí)間: 2025-3-30 10:50 作者: dictator 時(shí)間: 2025-3-30 14:01 作者: ligature 時(shí)間: 2025-3-30 19:18 作者: 事先無準(zhǔn)備 時(shí)間: 2025-3-30 21:34 作者: 不可接觸 時(shí)間: 2025-3-31 01:14 作者: 玩忽職守 時(shí)間: 2025-3-31 05:57 作者: 油氈 時(shí)間: 2025-3-31 11:54 作者: Kaleidoscope 時(shí)間: 2025-3-31 16:40
978-3-319-80765-2Springer International Publishing Switzerland 2016作者: 防銹 時(shí)間: 2025-3-31 21:29 作者: ILEUM 時(shí)間: 2025-3-31 22:24 作者: CAGE 時(shí)間: 2025-4-1 04:50 作者: 治愈 時(shí)間: 2025-4-1 09:24 作者: 協(xié)迫 時(shí)間: 2025-4-1 13:24
Introduction to Clinical Psychologys and viewpoints well suited to highly complex datasets. With the introduction of persistence and other geometric-topological ideas we can find and quantify local-to-global properties as well as quantifying qualitative changes in data.作者: 放肆的你 時(shí)間: 2025-4-1 16:50 作者: LINES 時(shí)間: 2025-4-1 21:43 作者: adipose-tissue 時(shí)間: 2025-4-2 00:32
Critical Appraisal of the Medical Literatureo optimal selection for transforming high-dimensional data into a low-dimensional space in a way that allows for their almost perfect reconstruction. The compression power along with the usage simplicity render CS an appealing method for optimal dimensionality reduction of big data. Although CS is r
