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2014 | Buch

Introduction Kapitel lesen Erstes Kapitel lesen

Big Data

Related Technologies, Challenges and Future Prospects

verfasst von: Min Chen, Shiwen Mao, Yin Zhang, Victor C.M. Leung

Verlag: Springer International Publishing

Buchreihe : SpringerBriefs in Computer Science

Enthalten in: Springer Professional "Wirtschaft+Technik" , Springer Professional "Technik" , Springer Professional "Wirtschaft"

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SUCHEN

Über dieses Buch

This Springer Brief provides a comprehensive overview of the background and recent developments of big data. The value chain of big data is divided into four phases: data generation, data acquisition, data storage and data analysis. For each phase, the book introduces the general background, discusses technical challenges and reviews the latest advances. Technologies under discussion include cloud computing, Internet of Things, data centers, Hadoop and more. The authors also explore several representative applications of big data such as enterprise management, online social networks, healthcare and medical applications, collective intelligence and smart grids. This book concludes with a thoughtful discussion of possible research directions and development trends in the field. Big Data: Related Technologies, Challenges and Future Prospects is a concise yet thorough examination of this exciting area. It is designed for researchers and professionals interested in big data or related research. Advanced-level students in computer science and electrical engineering will also find this book useful.

Inhaltsverzeichnis

Frontmatter
Chapter 1. Introduction
Abstract
The term of big data was coined under the explosive increase of global data and was mainly used to describe these enormous datasets. In this chapter, we introduce the definition of big data, and review its evolution in the past 20 years. In particular, we introduce the defining features of big data, as well as its 4Vs characteristics, including Volume, Variety, Velocity, and Value. The challenges brought about by big data is also examined in this chapter.
Min Chen, Shiwen Mao, Yin Zhang, Victor C. M. Leung
Chapter 2. Related Technologies
Abstract
In order to gain a deep understanding of big data, this chapter will introduce several fundamental technologies that are closely related to big data, including cloud computing, Internet of Things (IoT), data center, and Hadoop. For each related technology, a general introduction is first provided highlighting their key features. Then the relationship between the technology and big data is examined in detail.
Min Chen, Shiwen Mao, Yin Zhang, Victor C. M. Leung
Chapter 3. Big Data Generation and Acquisition
Abstract
We have introduced several key technologies related to big data, i.e., cloud computing, IoT, data center, and Hadoop. Next, we will focus on the value chain of big data, which can be generally divided into four phases: data generation, data acquisition, data storage, and data analysis. If we take data as a raw material, data generation and data acquisition are exploitation process, data storage is a storage process, and data analysis is a production process that utilizes the raw material to create new value.
Min Chen, Shiwen Mao, Yin Zhang, Victor C. M. Leung
Chapter 4. Big Data Storage
Abstract
In this chapter, we focus on the storage of big data. We will review important issues including massive storage systems, distributed storage systems, and big data storage mechanisms. On one hand, the storage infrastructure need to provide information storage service with reliable storage space; on the other hand, it must provide a powerful access interface for query and analysis of large amount of data. Such a storage infrastructure generally consists of hardware infrastructure and storage mechanisms.
Min Chen, Shiwen Mao, Yin Zhang, Victor C. M. Leung
Chapter 5. Big Data Analysis
Abstract
In this chapter, we introduce the methods, architectures and tools for big data analysis. The analysis of big data mainly involves analytical methods for traditional data and big data, analytical architecture for big data, and software used for mining and analysis of big data. Data analysis is the final and the most important phase in the value chain of big data, with the purpose of extracting useful values, providing suggestions or decisions. Different levels of potential values can be generated through the analysis of datasets in different fields.
Min Chen, Shiwen Mao, Yin Zhang, Victor C. M. Leung
Chapter 6. Big Data Applications
Abstract
In the previous chapter, we examined big data analysis, which is the final and most important phase of the value chain of big data. Big data analysis can provide useful values via judgments, recommendations, supports, or decisions. However, data analysis involves a wide range of applications, which frequently change and are extremely complex. In this chapter, the evolution of data sources is reviewed. Then, six of the most important data analysis fields are examined, including structured data analysis, text analysis, website analysis, multimedia analysis, network analysis, and mobile analysis. This chapter is concluded with a discussion of several key application fields of big data.
Min Chen, Shiwen Mao, Yin Zhang, Victor C. M. Leung
Chapter 7. Open Issues and Outlook
Abstract
In the previous chapters, we review the background and state-of-the-art of big data. In Fig. 7.1, it illustrates all the key technologies of big data introduced in this book. In this chapter, we summarize the research hot spots and suggest possible research directions of big data. We also discuss potential development trends in this broad research and application area.
Min Chen, Shiwen Mao, Yin Zhang, Victor C. M. Leung
Metadaten
Titel
Big Data
verfasst von
Min Chen
Shiwen Mao
Yin Zhang
Victor C.M. Leung
Copyright-Jahr
2014
Electronic ISBN
978-3-319-06245-7
Print ISBN
978-3-319-06244-0
DOI
https://doi.org/10.1007/978-3-319-06245-7