標(biāo)題: Titlebook: Big Data; A Primer Hrushikesha Mohanty,Prachet Bhuyan,Deepak Chenthat Book 2015 Springer India 2015 Big Data Applications.Machine Learning. [打印本頁] 作者: 黑暗社會(huì) 時(shí)間: 2025-3-21 18:18
書目名稱Big Data影響因子(影響力)
書目名稱Big Data影響因子(影響力)學(xué)科排名
書目名稱Big Data網(wǎng)絡(luò)公開度
書目名稱Big Data網(wǎng)絡(luò)公開度學(xué)科排名
書目名稱Big Data被引頻次
書目名稱Big Data被引頻次學(xué)科排名
書目名稱Big Data年度引用
書目名稱Big Data年度引用學(xué)科排名
書目名稱Big Data讀者反饋
書目名稱Big Data讀者反饋學(xué)科排名
作者: eczema 時(shí)間: 2025-3-21 21:38
Big Data Architecture, per analytic performed. There are three functional aspects to big data—data capture, data R&D, and data product. These three aspects must be placed in a framework for creating the data architecture. We discuss each of these aspects in depth in this chapter. The goal of big data is data-driven decis作者: AXIS 時(shí)間: 2025-3-22 02:45
Big Data Processing Algorithms,regation components for synchronization are some of the techniques being used in these algorithms to achieve the goals. MapReduce has been adopted in Big Data problems widely. This chapter demonstrates how MapReduce enables analytics to process massive data with ease. This chapter also provides exam作者: nominal 時(shí)間: 2025-3-22 07:37 作者: 壓倒性勝利 時(shí)間: 2025-3-22 08:58 作者: HACK 時(shí)間: 2025-3-22 16:51 作者: GRIPE 時(shí)間: 2025-3-22 17:17
https://doi.org/10.1007/978-1-4613-3843-7regation components for synchronization are some of the techniques being used in these algorithms to achieve the goals. MapReduce has been adopted in Big Data problems widely. This chapter demonstrates how MapReduce enables analytics to process massive data with ease. This chapter also provides exam作者: 放棄 時(shí)間: 2025-3-23 00:26 作者: 畏縮 時(shí)間: 2025-3-23 01:26 作者: bibliophile 時(shí)間: 2025-3-23 06:31 作者: Mettle 時(shí)間: 2025-3-23 11:57
Big Data Architecture,nal data to first interaction data and then sensor data. Web log was the first step in this evolution. These machines generated logs of internet activity caused the first growth of data. Social media pushed data production higher with human interactions. Automated observations and wearable technolog作者: 啟發(fā) 時(shí)間: 2025-3-23 16:49 作者: 國(guó)家明智 時(shí)間: 2025-3-23 18:39 作者: 惡意 時(shí)間: 2025-3-23 23:29 作者: 織布機(jī) 時(shí)間: 2025-3-24 03:59
Big Data Service Agreement,together usually referred as big data, though look heterogeneous and unrelated at a glance; still, big data carry striking relations among them implicitly as well as explicitly, so that many users across the world may get interested of data patterns being generated at a far end of the world. Thus, t作者: Abduct 時(shí)間: 2025-3-24 07:08
Applications of Big Data,d have been struggling to correlate the datasets and make any valuable business decisions. The key stumbling block has been the inability of the available systems to process large data when the data are part structured and part unstructured. As witnessed in the previous chapters, the technology stri作者: innate 時(shí)間: 2025-3-24 13:34
Hrushikesha Mohanty,Prachet Bhuyan,Deepak ChenthatIntroduces the subject to new entrants to the field, especially students at the senior undergraduate and graduate level.Introduces emerging computing paradigms for next generation Unstructured Data pr作者: 發(fā)展 時(shí)間: 2025-3-24 17:31
Studies in Big Datahttp://image.papertrans.cn/b/image/185575.jpg作者: Memorial 時(shí)間: 2025-3-24 21:34 作者: 通知 時(shí)間: 2025-3-25 02:16 作者: NOCT 時(shí)間: 2025-3-25 07:05 作者: 媒介 時(shí)間: 2025-3-25 08:10 作者: 偽造者 時(shí)間: 2025-3-25 12:56
https://doi.org/10.1007/978-1-4613-3843-7stributed across machines, the algorithms should also comply with the distributed storage. This chapter introduces some of the algorithms to work on such distributed storage and to scale with massive data. The algorithms, called Big Data Processing Algorithms, comprise random walks, distributed hash作者: GRAZE 時(shí)間: 2025-3-25 18:42
https://doi.org/10.1057/9781137355447high adoption of social media and networks by individuals. Since transactions on these sites are huge and increasing rapidly, social networks have become the new target for several business applications. Big Data mining deals with tapping large amount of data that is complex with a wide variety of d作者: homocysteine 時(shí)間: 2025-3-25 21:51 作者: 障礙 時(shí)間: 2025-3-26 02:36 作者: nutrition 時(shí)間: 2025-3-26 08:03 作者: 指耕作 時(shí)間: 2025-3-26 11:20 作者: nonchalance 時(shí)間: 2025-3-26 14:25 作者: 搜集 時(shí)間: 2025-3-26 20:25 作者: 拾落穗 時(shí)間: 2025-3-26 23:41
Security and Privacy of Big Data,researchers, policy makers, as well as professional/standards bodies. The book chapter would cover challenges, possible technologies, initiatives by stakeholders and emerging trends with respect to Security and Privacy.作者: innate 時(shí)間: 2025-3-27 04:28
Chika Trevor Sehoole,Jenny J. Leeg data processing. Finally, research issues in big data are identified. The references surveyed for this chapter introducing different facets of this emergent area in data science provide a lead to intending readers for pursuing their interests in this subject.作者: 變態(tài) 時(shí)間: 2025-3-27 05:56 作者: Guaff豪情痛飲 時(shí)間: 2025-3-27 10:33 作者: cauda-equina 時(shí)間: 2025-3-27 14:03
Big Data Service Agreement, an approach for SLA: service level agreement specification. SLA for a service is derived from SLAs of data consumer and provider on negotiation. Matching of consumer’s SLA to that of a provider SLA and a process of negotiation between these two are illustrated.作者: 龍蝦 時(shí)間: 2025-3-27 18:58 作者: SEED 時(shí)間: 2025-3-27 23:37
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