Large Scale and Big Data: Processing and Management (Hardcover)

Large Scale and Big Data: Processing and Management provides readers with a central source of reference on the data management techniques currently available for large-scale data processing. Presenting chapters written by leading researchers, academics, and practitioners, it addresses the fundamental challenges associated with Big Data processing tools and techniques across a range of computing environments.

The book begins by discussing the basic concepts and tools of large-scale Big Data processing and cloud computing. It also provides an overview of different programming models and cloud-based deployment models. The book’s second section examines the usage of advanced Big Data processing techniques in different domains, including semantic web, graph processing, and stream processing. The third section discusses advanced topics of Big Data processing such as consistency management, privacy, and security.

Supplying a comprehensive summary from both the research and applied perspectives, the book covers recent research discoveries and applications, making it an ideal reference for a wide range of audiences, including researchers and academics working on databases, data mining, and web scale data processing.

After reading this book, you will gain a fundamental understanding of how to use Big Data-processing tools and techniques effectively across application domains. Coverage includes cloud data management architectures, big data analytics visualization, data management, analytics for vast amounts of unstructured data, clustering, classification, link analysis of big data, scalable data mining, and machine learning techniques.

《大规模和大数据：处理与管理》为读者提供了一个关于目前可用于大规模数据处理的数据管理技术的中心参考来源。本书由领先的研究人员、学者和实践者撰写的章节，涵盖了在各种计算环境中处理大数据工具和技术所面临的基本挑战。

本书首先讨论了大规模大数据处理和云计算的基本概念和工具，并提供了不同编程模型和基于云的部署模型的概述。书的第二部分探讨了在不同领域中使用先进的大数据处理技术，包括语义网、图处理和流处理。第三部分讨论了大数据处理的高级主题，如一致性管理、隐私和安全性。

本书从研究和应用的角度提供了全面的总结，涵盖了最近的研究发现和应用，使其成为广泛受众的理想参考，包括从事数据库、数据挖掘和网络规模数据处理的研究人员和学者。

阅读本书后，您将获得如何在应用领域有效使用大数据处理工具和技术的基本理解。内容包括云数据管理架构、大数据分析可视化、数据管理、海量非结构化数据的分析、聚类、分类、大数据的链接分析、可扩展的数据挖掘和机器学习技术。