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Titlebook: Models of Computation for Big Data; Rajendra Akerkar Book 2018 The Author(s), under exclusive license to Springer Nature Switzerland AG 20

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發(fā)表于 2025-3-21 19:51:38 | 只看該作者 |倒序瀏覽 |閱讀模式
書目名稱Models of Computation for Big Data
編輯Rajendra Akerkar
視頻videohttp://file.papertrans.cn/637/636801/636801.mp4
概述Focuses on the fundamental principles of algorithm design for big data processing.Covers advanced models of computation relevant for developing memory-efficient algorithms.Advanced-level students and
叢書名稱Advanced Information and Knowledge Processing
圖書封面Titlebook: Models of Computation for Big Data;  Rajendra Akerkar Book 2018 The Author(s), under exclusive license to Springer Nature Switzerland AG 20
描述.The big data tsunami changes the perspective of industrial and academic research in how they address both foundational questions and practical applications. This calls for a paradigm shift in algorithms and?the underlying mathematical techniques. There is a need to understand foundational strengths and?address the state of the art challenges in big data that could lead to practical impact. The main goal of this book is to introduce algorithmic techniques for dealing with big data sets. Traditional algorithms work successfully when the input data fits well within memory. In many recent application situations, however, the size of the input data is too large to fit within memory...Models of Computation for Big Data,. covers mathematical models for developing such algorithms, which has its roots in the study of big data that occur often in various applications. Most techniques discussed come from research in the last decade. The book will be structured as a sequence of algorithmic ideas, theoretical underpinning, and practical use of that algorithmic idea. Intended for both graduate students and advanced undergraduate students, there are no formal prerequisites, but the reader should
出版日期Book 2018
關(guān)鍵詞Big Data Algorithms; Streaming Algorithms; Sublinear Time Algorithms; Algorithmic Techniquesfor Big Dat
版次1
doihttps://doi.org/10.1007/978-3-319-91851-8
isbn_softcover978-3-319-91850-1
isbn_ebook978-3-319-91851-8Series ISSN 1610-3947 Series E-ISSN 2197-8441
issn_series 1610-3947
copyrightThe Author(s), under exclusive license to Springer Nature Switzerland AG 2018
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1610-3947 ing memory-efficient algorithms.Advanced-level students and .The big data tsunami changes the perspective of industrial and academic research in how they address both foundational questions and practical applications. This calls for a paradigm shift in algorithms and?the underlying mathematical tech
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Book 2018ations. This calls for a paradigm shift in algorithms and?the underlying mathematical techniques. There is a need to understand foundational strengths and?address the state of the art challenges in big data that could lead to practical impact. The main goal of this book is to introduce algorithmic t
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Advanced Information and Knowledge Processinghttp://image.papertrans.cn/m/image/636801.jpg
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https://doi.org/10.1007/978-3-319-91851-8Big Data Algorithms; Streaming Algorithms; Sublinear Time Algorithms; Algorithmic Techniquesfor Big Dat
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978-3-319-91850-1The Author(s), under exclusive license to Springer Nature Switzerland AG 2018
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2512-5494 Schwerpunkten Internationales Management, Unternehmenspolitik und Corporate Governance, Führungskr?fte in Unternehmen sowie Entscheidungstr?ger in Aktion?rsvereinigungen und Investmentgesellschaften..978-3-409-12569-7978-3-322-90878-0Series ISSN 2512-5494 Series E-ISSN 2512-6490
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