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Titlebook: Data Science and Big Data Computing; Frameworks and Metho Zaigham Mahmood Book 2016 Springer International Publishing Switzerland 2016 Big

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發(fā)表于 2025-3-21 19:45:05 | 只看該作者 |倒序瀏覽 |閱讀模式
書目名稱Data Science and Big Data Computing
副標題Frameworks and Metho
編輯Zaigham Mahmood
視頻videohttp://file.papertrans.cn/264/263086/263086.mp4
概述Reviews the latest research and practice in data science and big data.Discusses tools and techniques for big data storage and analytics.Describes the frameworks relevant to data science, and their app
圖書封面Titlebook: Data Science and Big Data Computing; Frameworks and Metho Zaigham Mahmood Book 2016 Springer International Publishing Switzerland 2016 Big
描述This illuminating text/reference surveys the state of the art in data science, and provides practical guidance on big data analytics. Expert perspectives are provided by authoritative researchers and practitioners from around the world, discussing research developments and emerging trends, presenting case studies on helpful frameworks and innovative methodologies, and suggesting best practices for efficient and effective data analytics. Features: reviews a framework for fast data applications, a technique for complex event processing, and agglomerative approaches for the partitioning of networks; introduces a unified approach to data modeling and management, and a distributed computing perspective on interfacing physical and cyber worlds; presents techniques for machine learning for big data, and identifying duplicate records in data repositories; examines enabling technologies and tools for data mining; proposes frameworks for data extraction, and adaptive decision making and social media analysis.
出版日期Book 2016
關鍵詞Big Data Modeling and Management; Data Mining and Predictive Analytics; Security, Privacy, Safety and
版次1
doihttps://doi.org/10.1007/978-3-319-31861-5
isbn_softcover978-3-319-81139-0
isbn_ebook978-3-319-31861-5
copyrightSpringer International Publishing Switzerland 2016
The information of publication is updating

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Identifying Minimum-Sized Influential Vertices on Large-Scale Weighted Graphs: A Big Data Perspective among the types of data that can be abstracted as weighted graphs. Identifying minimum-sized influential vertices (MIV) in a weighted graph is an important task in graph mining that gains valuable commercial applications. Although different algorithms for this task have been proposed, it remains c
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Interfacing Physical and Cyber Worlds: A Big Data Perspectivelogies are progressing very rapidly, and computations are becoming an essential part of our life. Cyber-physical systems (CPSs) are a new evolution in computing that are integrated with the real world along with the physical devices to provide control in real-time environments. CPS generally takes i
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Large-Scale Data Analytics Tools: Apache Hive, Pig, and HBaseogramming models. It is designed to handle massive amounts of data and has the ability to store, analyze, and access large amounts of data quickly, across clusters of commodity hardware. Hadoop has several large-scale data processing tools and each has its own purpose. The Hadoop ecosystem has emerg
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Big Data Analytics: Enabling Technologies and Toolss mainly due to the unprecedented levels of technology adoption and adaption resulting in the connectivity technologies, network topologies, and tools that have enabled seamless connectivity between billions of physical, mechanical, electrical, electronic, and computer systems. This data explosion a
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