標(biāo)題: Titlebook: Big Data Analytics; Methods and Applicat Saumyadipta Pyne,B.L.S. Prakasa Rao,S.B. Rao Book 2016 Springer India 2016 Big Data.Computational [打印本頁(yè)] 作者: 涌出 時(shí)間: 2025-3-21 16:50
書(shū)目名稱Big Data Analytics影響因子(影響力)
書(shū)目名稱Big Data Analytics影響因子(影響力)學(xué)科排名
書(shū)目名稱Big Data Analytics網(wǎng)絡(luò)公開(kāi)度
書(shū)目名稱Big Data Analytics網(wǎng)絡(luò)公開(kāi)度學(xué)科排名
書(shū)目名稱Big Data Analytics被引頻次
書(shū)目名稱Big Data Analytics被引頻次學(xué)科排名
書(shū)目名稱Big Data Analytics年度引用
書(shū)目名稱Big Data Analytics年度引用學(xué)科排名
書(shū)目名稱Big Data Analytics讀者反饋
書(shū)目名稱Big Data Analytics讀者反饋學(xué)科排名
作者: 上坡 時(shí)間: 2025-3-21 20:22 作者: aesthetician 時(shí)間: 2025-3-22 01:08 作者: 食料 時(shí)間: 2025-3-22 04:34
Application of Mixture Models to Large Datasets,sider normal and .-mixture models. As they are highly parameterized, we review methods to enable them to be fitted to large datasets involving many observations and variables. Attention is then given to extensions of these mixture models to mixtures with skew normal and skew .-distributions for the 作者: deadlock 時(shí)間: 2025-3-22 09:28 作者: Spinal-Fusion 時(shí)間: 2025-3-22 14:14 作者: 失眠癥 時(shí)間: 2025-3-22 20:11 作者: Systemic 時(shí)間: 2025-3-23 00:39 作者: 修飾語(yǔ) 時(shí)間: 2025-3-23 03:08 作者: 不能逃避 時(shí)間: 2025-3-23 06:33
Application-Level Benchmarking of Big Data Systems, phenomenon. The amount, rate, and variety of data that are assembled—for almost any application domain—are necessitating a reexamination of old technologies and development of new technologies to get value from the data, in a timely fashion. With increasing adoption and penetration of mobile techno作者: FICE 時(shí)間: 2025-3-23 11:15 作者: anagen 時(shí)間: 2025-3-23 17:57
Microbiome Data Mining for Microbial Interactions and Relationships,logy science and the understanding of human health and diseases. Researchers have started to infer common interspecies interactions and species–phenotype relations such as competitive and cooperative interactions leveraging to big microbiome data. These endeavors have facilitated the discovery of pr作者: 最低點(diǎn) 時(shí)間: 2025-3-23 21:05 作者: 異端 時(shí)間: 2025-3-24 01:04
as in Big Data applications such as management, Internet of .This book has a collection of articles written by Big Data experts to describe some of the cutting-edge methods and applications from their respective areas of interest, and provides the reader with a?detailed overview of the field of Big 作者: 外星人 時(shí)間: 2025-3-24 03:35 作者: promote 時(shí)間: 2025-3-24 08:00 作者: 炸壞 時(shí)間: 2025-3-24 14:18 作者: 諷刺滑稽戲劇 時(shí)間: 2025-3-24 17:11 作者: BET 時(shí)間: 2025-3-24 21:07
Modern Approaches in Solid Earth Sciencesmethods. Two methods—Symbolic Data Analysis and Approximate Stream Regression—which holds promise in addressing some of the challenges with Big Data are discussed briefly with real life examples. Two case studies of applications of analytics in management—one in marketing management and the other in human resource management—are discussed.作者: 處理 時(shí)間: 2025-3-25 01:58 作者: strain 時(shí)間: 2025-3-25 06:34 作者: 自制 時(shí)間: 2025-3-25 08:45
Argumente für das Intrapreneuringcases, characterize the trade-offs between processing latency and data volume capacity of contemporary big data platforms, and discuss the critical role that . and . systems play in addressing the analytics needs of IoT applications.作者: 動(dòng)物 時(shí)間: 2025-3-25 14:06
Book 2016e areas of interest, and provides the reader with a?detailed overview of the field of Big Data Analytics as it is practiced today. The chapters?cover technical aspects of key areas?that generate and use?Big Data such as management and?finance; medicine and?healthcare; genome, cytome and microbiome;?作者: Ganglion 時(shí)間: 2025-3-25 17:46
Big Data Analytics Platforms for Real-Time Applications in IoT,cases, characterize the trade-offs between processing latency and data volume capacity of contemporary big data platforms, and discuss the critical role that . and . systems play in addressing the analytics needs of IoT applications.作者: rheumatism 時(shí)間: 2025-3-25 20:06 作者: exceed 時(shí)間: 2025-3-26 00:42 作者: infantile 時(shí)間: 2025-3-26 05:02 作者: 憤慨點(diǎn)吧 時(shí)間: 2025-3-26 11:39 作者: 鞠躬 時(shí)間: 2025-3-26 13:09 作者: anniversary 時(shí)間: 2025-3-26 18:55
Microbiome Data Mining for Microbial Interactions and Relationships,putational efforts in microbiome data mining for discovering microbial interactions and relationships including dimension reduction and data visualization, association analysis, microbial network reconstruction, as well as dynamic modeling and simulations.作者: Ptosis 時(shí)間: 2025-3-26 23:01
https://doi.org/10.1007/978-81-322-3628-3Big Data; Computational Statistics; Data Mining; High-dimensional Data; Data Science作者: 冬眠 時(shí)間: 2025-3-27 01:16 作者: Distribution 時(shí)間: 2025-3-27 06:30 作者: PIZZA 時(shí)間: 2025-3-27 12:11
Modern Approaches in Solid Earth Sciences can be profitably used for decision making. Aided by low-cost computer hardware, fast processing speeds and advancements in data storage technologies, Big Data Analytics has emerged as a fast growing field. However, the statistical challenges that are faced by statisticians and data scientists, whi作者: Rotator-Cuff 時(shí)間: 2025-3-27 14:29 作者: 帽子 時(shí)間: 2025-3-27 21:51 作者: 難聽(tīng)的聲音 時(shí)間: 2025-3-28 01:37
Sponsoren finden, die neue Ideen schützentitioning is often formalized as an optimization problem where we assign graph vertices to computing nodes with the objection to both minimize the communication cost between computing nodes and to balance the load of computing nodes. Such optimization was specified using a cost function to measure t作者: 悲痛 時(shí)間: 2025-3-28 03:05 作者: NADIR 時(shí)間: 2025-3-28 09:38 作者: Fortuitous 時(shí)間: 2025-3-28 11:48
https://doi.org/10.1007/978-3-322-94468-9acy-preserving P2P traffic detectors. The proposed detectors do not rely on payload signatures, and hence, are resilient to P2P client and protocol changes—a phenomenon which is now becoming increasingly frequent with newer, more popular P2P clients/protocols. The article also discusses newer design作者: CLOWN 時(shí)間: 2025-3-28 18:35
https://doi.org/10.1007/978-3-662-64102-6 phenomenon. The amount, rate, and variety of data that are assembled—for almost any application domain—are necessitating a reexamination of old technologies and development of new technologies to get value from the data, in a timely fashion. With increasing adoption and penetration of mobile techno作者: Flirtatious 時(shí)間: 2025-3-28 22:15
Tobias Schl?mer,Karina Kiepe,Tim Thrunir volume, velocity, and variety (the 3 “V”s). Volume is a major concern for EHRs especially due to the presence of huge amount of null data, i.e., for storing sparse data that leads to storage wastage. Reducing storage wastage due to sparse values requires amendments to the storage mechanism that s作者: 放逐 時(shí)間: 2025-3-29 02:29 作者: 改良 時(shí)間: 2025-3-29 04:56
Tobias Schl?mer,Karina Kiepe,Tim Thrunly to explore the relationship between large-scale neural and behavorial data. In this chapter, we present a computationally efficient nonlinear technique which can be used for big data analysis. We demonstrate the efficacy of our method in the context of brain computer interface. Our technique is p作者: modifier 時(shí)間: 2025-3-29 10:25 作者: 不能和解 時(shí)間: 2025-3-29 12:22
Saumyadipta Pyne,B.L.S. Prakasa Rao,S.B. RaoIntroduces new computational methods and key applications due to known international researchers and labs.Provides different application areas in Big Data applications such as management, Internet of 作者: 沒(méi)收 時(shí)間: 2025-3-29 17:39 作者: 顯微鏡 時(shí)間: 2025-3-29 20:35 作者: Corroborate 時(shí)間: 2025-3-30 03:51
https://doi.org/10.1007/978-3-662-64102-6The advent of high-throughput technology has revolutionized biological sciences in the last two decades enabling experiments on the whole genome scale. Data from such large-scale experiments are interpreted at system’s level to understand the interplay among genome, transcriptome, epigenome, proteome, metabolome, and regulome.作者: 物種起源 時(shí)間: 2025-3-30 07:03 作者: 雄辯 時(shí)間: 2025-3-30 08:43 作者: Creatinine-Test 時(shí)間: 2025-3-30 13:33
An Efficient Partition-Repetition Approach in Clustering of Big Data,ial emphasis on the characteristic of big data in this context. We develop an adaptation of a standard method that is more suitable to big data clustering when the data cloud is relatively regular with respect to inherent features. We also discuss a novel method for some special types of data where 作者: 直覺(jué)好 時(shí)間: 2025-3-30 19:56
Online Graph Partitioning with an Affine Message Combining Cost Function, developed in order to minimize communication of computing nodes, should be considered in designing new cost functions for graph partitioning. In this paper, we propose a new cost function for graph partitioning which considers message combining. In this new cost function, we consider communication 作者: 共同生活 時(shí)間: 2025-3-31 00:06
Application-Level Benchmarking of Big Data Systems,esearch, there is a pressing need for benchmarks that can provide objective evaluations of alternative technologies and solution approaches to a given big data problem. This chapter gives an introduction to big data benchmarking and presents different proposals and standardization efforts.作者: 遍及 時(shí)間: 2025-3-31 01:06
Managing Large-Scale Standardized Electronic Health Records,em for managing large-scale standardized records in terms of data volume, velocity, and variety. Every proposed modification to logical layer has its pros and cons. In this chapter, we will discuss various aspects of the solutions proposed for managing standardized EHRs, and the approaches to adopt
