Big Data Computing

Title:

Big Data Computing

Physical Description:

xxii, 542 pages : illustrations ; 24 cm.

ISBN:

9780367379117

Abstract:

Due to market forces and technological evolution, Big Data computing is developing at an increasing rate. A wide variety of novel approaches and tools have emerged to tackle the challenges of Big Data, creating both more opportunities and more challenges for students and professionals in the field of data computation and analysis

Subject Term:

Added Author:

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Library	Item Barcode	Call Number	Material Type	Item Category 1	Status
Searching... PSZ JB	30000010371653	QA76.9.D3 B544 2014	Open Access Book	Book	Searching... Unknown
Searching... PSZ KL	33000000006751	QA76.9.D3 B544 2014	Open Access Book	Book	Searching... Unknown

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Summary

Presenting a mix of industry cases and theory, Big Data Computing discusses the technical and practical issues related to Big Data in intelligent information management. Emphasizing the adoption and diffusion of Big Data tools and technologies in industry, the book introduces a broad range of Big Data concepts, tools, and techniques. It covers a wide range of research, and provides comparisons between state-of-the-art approaches. 　

Comprised of five sections, the book focuses on:

What Big Data is and why it is important Semantic technologies Tools and methods Business and economic perspectives Big Data applications across industries

Author Notes

Rajendra Akerkar

Vadim Ermolayev and Rajendra Akerkar and Vagan Terziyan and Michael CochezPierfrancesco Bellini and Mariano di Claudio and Paolo Nesi and Nadia RauchRoberto V. ZicariJavier D. Fernández and Mario Arias and Miguel A. Martínez-Prieto and Claudio GutiérrezSören Auer and Axel-Cyrille Ngonga Ngomo and Philipp Frischmuth and Jakub KlimekMartin Giese and Diego Calvanese and Peter Haase and Ian Horrocks and Yannis Ioannidis and Herald Kllapi and Manolis Koubarakis and Maurizio Lenzerini and Rolf Möller and Mariano Rodriguez Muro and Özgür Özcep and Riccardo Rosati and Rudolf Schlatte and Michael Schmidt and Ahmet Soylu and Arild WaalerHele-Mai Haav and Peep KüngasStratos IdreosJordà PoloM. Asif Naeem and Gillian Dobbie and Gerald WeberTassilo PellegrinRajendra AkerkarErik Cambria and Dheeraj Rajagopal and Daniel Olsher and Dipankar DasDario Bonino and Fulvio Corno and Luigi De RussisMikhail Simonov and Giuseppe Caragnano and Lorenzo Mossucca and Pietro Ruiu and Olivier TerzoBin Jiang and Xintao LiuMarcus Spies and Monika Jungemann-Dorner

Preface	p. ix
Editor	p. xvii
Contributors	p. xix
Section I Introduction
1 Toward Evolving Knowledge Ecosystems for Big Data Understanding	p. 3
2 Tassonomy and Review of Big Data Solutions Navigation	p. 57
3 Big Data: Challenges and Opportunities	p. 103
Section II Semantic Technologies and Big Data
4 Management of Big Semantic Data	p. 131
5 Linked Data in Enterprise Integration	p. 169
6 Scalable End-User Access to Big Data	p. 205
7 Semantic Data Interoperability: The Key Problem of Big Data	p. 245
Section III Big Data Processing
8 Big Data Exploration	p. 273
9 Big Data Processing with MapReduce	p. 295
10 Efficient Processing of Stream Data over Persistent Data	p. 315
Section IV Big Data and Business
11 Economics of Big Data: A Value Perspective on State of the Art and Future Trends	p. 343
12 Advanced Data Analytics for Business	p. 373
Section V Big Data Applications
13 Big Social Data Analysis	p. 401
14 Real-Time Big Data Processing for Domain Experts: An Application to Smart Buildings	p. 415
15 Big Data Application: Analyzing Real-Time Electric Meter Data	p. 449
16 Scaling of Geographic Space from the Perspective of City and Field Blocks and Using Volunteered Geographic Information	p. 483
17 Big Textual Data Analytics and Knowledge Management	p. 501
Index	p. 539

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Summary

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Table of Contents