Big Data Computing

To tackle the challenges of Big Data, novel approaches and tools have emerged. The technology required for big-data computing is developing at a satisfactory rate due to market forces and technological evolution. This book presents a mix of theory and real world cases that discuss the technical and practical issues related to Big Data in intelligent information management. It emphasizes the adoption and diffusion of Big Data tools and technologies in real practical applications. In addition, the book balances between academic and industry contributions.

In the international marketplace, businesses, suppliers and customers are creating and consuming vast amounts of information. Gartner predicts that enterprise data in all forms will grow 650 percent over the next five years. According to IDC, the world's volume of data doubles every 18 months. This deluge of data, often referred to as “big data,” obviously creates a challenge for business community and data scientists. Big data has now reached every sector in the world economy. Big Data is transforming competitive opportunities in every industry sector including banking, healthcare, insurance, manufacturing, retail, wholesale, transportation, communications, construction, education, and utilities. It also plays key roles in trade operations such as marketing, operations, supply chain, and new business models. It is becoming quite clear that enterprises that fail to use their data efficiently are at a substantial competitive disadvantage from those that can analyze and act on their data. The possibilities of big data continue to evolve swiftly, driven by innovation in the underlying technologies, platforms, and analytic capabilities for handling data, as well as the evolution of behavior among its users as increasingly folks live digital lives. To tackle the challenges of Big Data, novel approaches and tools have emerged. Moreover, the technology required for big‐data computing is developing at a satisfactory rate due to market forces and technological evolution.

This book contains contributions from outstanding researchers on the Big Data and presents detailed background, discussion, and illustration of innovative approaches, technologies and applications for solving the problems in analysis of Big Data. We believe it can trigger some new ideas for practical Big Data applications.

Preface
Editor
List of Contributors

Part I     INTRODUCTION

1.    Towards Evolving Knowledge Ecosystems for Big Data Understanding
        Vadim Ermolayev, Rajendra Akerkar, Vagan Terziyan, Michael Cochez

2.    Tassonomy and Review of Big Data Solutions Navigation
        Pierfrancesco Bellini, Mariano Di Claudio, Paolo Nesi, Nadia Rauch

3.    Big Data: Challenges and Opportunity
    Roberto V. Zicari

Part II    SEMANTIC TECHNOLOGIES & BIG DATA

4.    Management of Big Semantic Data
       Javier D. Fernández, Mario Arias, Miguel A. Martínez-Prieto, Claudio Gutiérrez

5.    Linked Data in Enterprise Integration
       Sören Auer, Axel-Cyrille Ngonga Ngomo, Philipp Frischmuth , Jakub Klimek

6.    Scalable End-user Access to Big Data
        Martin Giese, Diego Calvanese, Ian Horrocks, Yannis Ioannidis, Herald Klappi, Manolis Koubarakis, Maurizio Lenzerini,
          Ralf Möller, Özgür Özçep, Mariano Rodriguez Muro, Riccardo Rosati, Rudolf Schlatte, Peter Haase, Michael Schmidt,
          Ahmet Soylu, Arild Waaler,

7.    Semantic Data Interoperability: the Key Problem of Big Data
        Hele-Mai Haav, Peep Küngas

Part III     BIG DATA PROCESSING

8.    Big Data Exploration
       Stratos Idreos

9.    Big Data Processing with MapReduce
      Jordà Polo

10.    Efficient Processing of Stream Data over Persistent Data
       Gerald Weber, M. Asif Naeem, Gillian Dobbie

Part IV     BIG DATA & BUSINESS

11.    The Economics of Big Data – A Value Perspective on State of the Art and Future Trends
         Tassilo Pellegrin

12.    Advanced Data Analytics for Business
         Rajendra Akerkar

Part V      BIG DATA APPLICATIONS

13.    Big Social Data Analysis
         Erik Cambria, Dheeraj Rajagopal, Daniel Olsher, Dipankar Das

14.    Real-time Big Data Processing for Domain Experts: An Application to Smart Buildings
         Dario Bonino, Fulvio Corno, Luigi De Russis

15.    Big Data Application: Analyzing Real Time Electric Meter Data
         Mikhail Simonov, Giuseppe Caragnano, Lorenzo Mossucca, Pietro Ruiu, Olivier Terzo

16.    Scaling of Geographic Space from the Perspective of City and Field Blocks and Using
         Volunteered Geographic Information
         Bin Jiang

17.    Big Textual Data Analytics and Knowledge Management
         Marcus Spies, Monika Jungemann-Dorner

Index

The aim of this book is to be accessible to researchers, graduate students, and to application-driven practitioners who work in data science and related fields. This edited book requires no previous exposure to large-scale data analysis or NoSQL tools. Acquaintance with traditional databases is helpful.
The technical level of this book also makes it accessible to students taking advanced undergraduate level courses on Big Data or Data Science.
Besides, the goal is to help policy makers, developers and engineers, data scientists and individuals navigate the new Big Data landscape.

Big Data Computing Edited by Rajendra Akerkar Western Norway Research Institute, Norway Publisher: Taylor & Francis Group/CRC Press Hardback: 978-1-46-657837-1 eBook: 978-1-46-657838-8
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