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Titlebook: Cognitive Computing for Big Data Systems Over IoT; Frameworks, Tools an Arun Kumar Sangaiah,Arunkumar Thangavelu,Venkatesa Book 2018 The Ed

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書(shū)目名稱Cognitive Computing for Big Data Systems Over IoT
副標(biāo)題Frameworks, Tools an
編輯Arun Kumar Sangaiah,Arunkumar Thangavelu,Venkatesa
視頻videohttp://file.papertrans.cn/230/229008/229008.mp4
概述Explores domain knowledge and data reasoning technologies and cognitive methods using the Internet of Things (IoTs).Focuses on the design of the best cognitive embedded data technologies to process an
叢書(shū)名稱Lecture Notes on Data Engineering and Communications Technologies
圖書(shū)封面Titlebook: Cognitive Computing for Big Data Systems Over IoT; Frameworks, Tools an Arun Kumar Sangaiah,Arunkumar Thangavelu,Venkatesa Book 2018 The Ed
描述.This book brings a high level of fluidity to analytics and addresses recent trends, innovative ideas, challenges and cognitive computing solutions in big data and the Internet of Things (IoT). It explores domain knowledge, data science reasoning and cognitive methods in the context of the IoT, extending current data science approaches by incorporating insights from experts as well as a notion of artificial intelligence, and performing inferences on the knowledge.The book provides a comprehensive overview of the constituent paradigms underlying cognitive computing methods, which illustrate the increased focus on big data in IoT problems as they evolve. It includes novel, in-depth fundamental research contributions from a methodological/application in data science accomplishing sustainable solution for the future perspective...Mainly focusing on the design of the best cognitive embedded data science technologies to process and analyze the large amount of data collectedthrough the IoT, and aid better decision making, the book discusses adapting decision-making approaches under cognitive computing paradigms to demonstrate how the proposed procedures as well as big data and IoT problem
出版日期Book 2018
關(guān)鍵詞Cognitive Computing; Big Data Analysis; Internet of Things; Data Analytics; Data Technologies; Cognitive
版次1
doihttps://doi.org/10.1007/978-3-319-70688-7
isbn_softcover978-3-319-70687-0
isbn_ebook978-3-319-70688-7Series ISSN 2367-4512 Series E-ISSN 2367-4520
issn_series 2367-4512
copyrightThe Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerl
The information of publication is updating

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https://doi.org/10.1007/978-3-031-41163-2omposed of thousands distributed entities and a number of multimedia smart devices. In recent years, due to the improvement of popularity and capability of smart mobile devices, Mobile Cloud Computing (MCC) gains a considerable attention in Internet of Things (IoT) environment. As there are variety
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B. A. Cerda,M. J. Nold,C. Wesdemiotisntribute to very challenging area for data science method applying. Traditional approach of predictive modelling became insufficient, because relaying on few variables as a base of the fraud model are very fragile concept. Reason for that is fact that we are talking about portfolio with low cases of
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Shuxiang Guo,Nan Xiao,Baofeng GaooT procedures. Its purpose is to expand the range of entities to be considered when describing a sensor-monitored environment by allowing, in particular, to seamlessly model in a unified way (i.e., within the same representation framework) physical entities like objects, humans, robots, etc. and hig
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