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Big Data

Big Data Book
Author : Viktor Mayer-Schönberger,Kenneth Cukier
Publisher : Houghton Mifflin Harcourt
Release : 2013-03-05
ISBN : 0544002938
Language : En, Es, Fr & De

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Book Description :

A revelatory exploration of the hottest trend in technology and the dramatic impact it will have on the economy, science, and society at large. Which paint color is most likely to tell you that a used car is in good shape? How can officials identify the most dangerous New York City manholes before they explode? And how did Google searches predict the spread of the H1N1 flu outbreak? The key to answering these questions, and many more, is big data. “Big data” refers to our burgeoning ability to crunch vast collections of information, analyze it instantly, and draw sometimes profoundly surprising conclusions from it. This emerging science can translate myriad phenomena—from the price of airline tickets to the text of millions of books—into searchable form, and uses our increasing computing power to unearth epiphanies that we never could have seen before. A revolution on par with the Internet or perhaps even the printing press, big data will change the way we think about business, health, politics, education, and innovation in the years to come. It also poses fresh threats, from the inevitable end of privacy as we know it to the prospect of being penalized for things we haven’t even done yet, based on big data’s ability to predict our future behavior. In this brilliantly clear, often surprising work, two leading experts explain what big data is, how it will change our lives, and what we can do to protect ourselves from its hazards. Big Data is the first big book about the next big thing. www.big-data-book.com

Big Data a Very Short Introduction

Big Data  a Very Short Introduction Book
Author : Dawn E. Holmes
Publisher : Oxford University Press
Release : 2017-10-23
ISBN : 0198779577
Language : En, Es, Fr & De

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Book Description :

Since long before computers were even thought of, data has been collected and organized by diverse cultures across the world. Once access to the Internet became a reality for large swathes of the world's population, the amount of data generated each day became huge, and continues to grow exponentially. It includes all our uploaded documents, video, and photos, all our social media traffic, our online shopping, even the GPS data from our cars. "Big Data" represents a qualitative change, not simply a quantitative one. The term refers both to the new technologies involved, and to the way it can be used by business and government. Dawn E. Holmes uses a variety of case studies to explain how data is stored, analyzed, and exploited by a variety of bodies from big companies to organizations concerned with disease control. Big data is transforming the way businesses operate, and the way medical research can be carried out. At the same time, it raises important ethical issues; Holmes discusses cases such as the Snowden affair, data security, and domestic smart devices which can be hijacked by hackers. ABOUT THE SERIES: The Very Short Introductions series from Oxford University Press contains hundreds of titles in almost every subject area. These pocket-sized books are the perfect way to get ahead in a new subject quickly. Our expert authors combine facts, analysis, perspective, new ideas, and enthusiasm to make interesting and challenging topics highly readable.

Big Data and HPC Ecosystem and Convergence

Big Data and HPC  Ecosystem and Convergence Book
Author : L. Grandinetti,S.L. Mirtaheri,R. Shahbazian
Publisher : IOS Press
Release : 2018-08-22
ISBN : 1614998825
Language : En, Es, Fr & De

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Book Description :

Due to the increasing need to solve complex problems, high-performance computing (HPC) is now one of the most fundamental infrastructures for scientific development in all disciplines, and it has progressed massively in recent years as a result. HPC facilitates the processing of big data, but the tremendous research challenges faced in recent years include: the scalability of computing performance for high velocity, high variety and high volume big data; deep learning with massive-scale datasets; big data programming paradigms on multi-core; GPU and hybrid distributed environments; and unstructured data processing with high-performance computing. This book presents 19 selected papers from the TopHPC2017 congress on Advances in High-Performance Computing and Big Data Analytics in the Exascale era, held in Tehran, Iran, in April 2017. The book is divided into 3 sections: State of the Art and Future Scenarios, Big Data Challenges, and HPC Challenges, and will be of interest to all those whose work involves the processing of Big Data and the use of HPC.

Thick Big Data

Thick Big Data Book
Author : Dariusz Jemielniak
Publisher : Oxford University Press, USA
Release : 2020-02
ISBN : 0198839707
Language : En, Es, Fr & De

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Book Description :

The social sciences are becoming datafied. The questions once considered the domain of sociologists are now answered by data scientists operating on large datasets and breaking with methodological tradition, for better or worse. The traditional social sciences, such as sociology or anthropology, are under the double threat of becoming marginalized or even irrelevant, both from new methods of research which require more computational skills and from increasing competition from the corporate world which gains an additional advantage based on data access. However, unlike data scientists, sociologists and anthropologists have a long history of doing qualitative research. The more quantified datasets we have, the more difficult it is to interpret them without adding layers of qualitative interpretation. Big Data therefore needs Thick Data. This book presents the available arsenal of new methods and tools for studying society both quantitatively and qualitatively, opening ground for the social sciences to take the lead in analysing digital behaviour. It shows that Big Data can and should be supplemented and interpreted through thick data as well as cultural analysis. Thick Big Data is critically important for students and researchers in the social sciences to understand the possibilities of digital analysis, both in the quantitative and qualitative area, and to successfully build mixed-methods approaches.

Big Data on Campus

Big Data on Campus Book
Author : Karen L. Webber,Henry Y. Zheng
Publisher : Johns Hopkins University Press
Release : 2020-11-03
ISBN : 1421439034
Language : En, Es, Fr & De

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Book Description :

Webber, Henry Y. Zheng, Ying Zhou

Big Data Mining for Climate Change

Big Data Mining for Climate Change Book
Author : Zhihua Zhang
Publisher : Unknown
Release : 2019-12
ISBN : 0128187034
Language : En, Es, Fr & De

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Book Description :

Big Data Mining for Climate Change addresses how to manage the vast amount of information available for analysis. Climate change and its environmental, economic and social consequences are widely recognized as the biggest, most interconnected problem facing humanity. There is a huge amount of potential information currently available...and it is growing exponentially. This book walks through the latest research and how to navigate the resources available using big data applications. It is appropriate for scientists and advanced students studying climate change from a number of disciplines, including the atmospheric sciences, oceanic sciences, geography, environment sciences, ecology, energy, economics, engineering and public policy. Provides a step-by-step guide for applying big data mining tools to climate and environmental research Presents a comprehensive review of theory and algorithms of big data mining for climate change Includes current research in climate and environmental science as it relates to using big data algorithms

The Enterprise Big Data Lake

The Enterprise Big Data Lake Book
Author : Alex Gorelik
Publisher : O'Reilly Media
Release : 2019-02-21
ISBN : 1491931523
Language : En, Es, Fr & De

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Book Description :

Enterprises are experimenting with using Hadoop to build Big Data Lakes, but many projects are stalling or failing because the approaches that worked at Internet companies have to be adopted for the enterprise. This practical handbook guides managers and IT professionals from the initial research and decision-making process through planning, choosing products, and implementing, maintaining, and governing the modern data lake. You'll explore various approaches to starting and growing a Data Lake, including Data Warehouse off-loading, analytical sandboxes, and "Data Puddles." Author Alex Gorelik shows you methods for setting up different tiers of data, from raw untreated landing areas to carefully managed and summarized data. You'll learn how to enable self-service to help users find, understand, and provision data; how to provide different interfaces to users with different skill levels; and how to do all of that in compliance with enterprise data governance policies.

Big Data Analytics in Bioinformatics and Healthcare

Big Data Analytics in Bioinformatics and Healthcare Book
Author : Baoying Wang,Ruowang Li,William Perrizo
Publisher : Medical Info Science Reference
Release : 2014-10-31
ISBN : 9781466666115
Language : En, Es, Fr & De

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Book Description :

"This book merges the fields of biology, technology, and medicine in order to present a comprehensive study on the emerging information processing applications necessary in the field of electronic medical record management"--Provided by publisher.

Understanding Big Data Analytics for Enterprise Class Hadoop and Streaming Data

Understanding Big Data  Analytics for Enterprise Class Hadoop and Streaming Data Book
Author : Paul Zikopoulos,Chris Eaton
Publisher : McGraw Hill Professional
Release : 2011-10-22
ISBN : 0071790543
Language : En, Es, Fr & De

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Book Description :

Big Data represents a new era in data exploration and utilization, and IBM is uniquely positioned to help clients navigate this transformation. This book reveals how IBM is leveraging open source Big Data technology, infused with IBM technologies, to deliver a robust, secure, highly available, enterprise-class Big Data platform. The three defining characteristics of Big Data--volume, variety, and velocity--are discussed. You'll get a primer on Hadoop and how IBM is hardening it for the enterprise, and learn when to leverage IBM InfoSphere BigInsights (Big Data at rest) and IBM InfoSphere Streams (Big Data in motion) technologies. Industry use cases are also included in this practical guide. Learn how IBM hardens Hadoop for enterprise-class scalability and reliability Gain insight into IBM's unique in-motion and at-rest Big Data analytics platform Learn tips and tricks for Big Data use cases and solutions Get a quick Hadoop primer

Big Data

Big Data Book
Author : Balamurugan Balusamy,Nandhini Abirami R,Amir H. Gandomi
Publisher : John Wiley & Sons
Release : 2021-04-27
ISBN : 1119701821
Language : En, Es, Fr & De

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Book Description :

This book offers comprehensive coverage of Big Data tools, terminologies and technologies for researchers, business professionals and graduates. This book begins with an overview of what Big Data is and emphasizes all the key concepts of big data end to end. Big Data concepts, technologies, terminologies and storing, processing and analysis techniques and much more – are all logically organized and reinforced by diagrams and case studies. This book refines readers’ understanding of Big Data with in-depth analysis of key concepts. The case studies provided in this book give insight on key concepts. The initial chapters of the book shed light on various characteristics of Big Data that distinguish it from traditional Database Management systems. Big Data Analytics are covered in detail in a separate chapter. Hadoop, the heart of Big Data is handled in the Big Data processing chapter and a deep understanding of its concepts is provided.

Big Data Little Data No Data

Big Data  Little Data  No Data Book
Author : Christine L. Borgman
Publisher : MIT Press
Release : 2015
ISBN : 0262028565
Language : En, Es, Fr & De

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Book Description :

An examination of the uses of data within a changing knowledge infrastructure, offering analysis and case studies from the sciences, social sciences, and humanities. “Big Data” is on the covers of Science, Nature, the Economist, and Wired magazines, on the front pages of the Wall Street Journal and the New York Times. But despite the media hyperbole, as Christine Borgman points out in this examination of data and scholarly research, having the right data is usually better than having more data; little data can be just as valuable as big data. In many cases, there are no data—because relevant data don't exist, cannot be found, or are not available. Moreover, data sharing is difficult, incentives to do so are minimal, and data practices vary widely across disciplines. Borgman, an often-cited authority on scholarly communication, argues that data have no value or meaning in isolation; they exist within a knowledge infrastructure—an ecology of people, practices, technologies, institutions, material objects, and relationships. After laying out the premises of her investigation—six “provocations” meant to inspire discussion about the uses of data in scholarship—Borgman offers case studies of data practices in the sciences, the social sciences, and the humanities, and then considers the implications of her findings for scholarly practice and research policy. To manage and exploit data over the long term, Borgman argues, requires massive investment in knowledge infrastructures; at stake is the future of scholarship.

Big Data Analytics with Vehicle Data

Big Data Analytics with Vehicle Data Book
Author : Ashok Singamaneni
Publisher : Unknown
Release : 2016
ISBN : 9781369139341
Language : En, Es, Fr & De

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Book Description :

Many companies have invested a lot over the past decade just to collect the data and store them in a cloud. However collection of such large amount of data will be justified only when there are some useful insights drawn from them. There is a lot of data collected from vehicles. The volume, velocity, variability and complexity of the data from various sensors are massive. Access to this type of data is only going to increase with time, so industries need appropriate methods to transform this raw data into insights and knowledge. Extraction of insights which were previously unknown or potentially useful patterns or knowledge from this kind of these massive amounts of data can only be achieved by using Big Data analytics. Conventional software cannot handle the robustness of these, so modern tools such as Hadoop and Knime were used in this thesis to analyze the data. Raw high resolution data was used and a model was developed to understand vehicle/customer behaviors and then compared and contrasted. This thesis involves found a proper method for identifying and calculating the principal attributes that accurately and efficiently characterize a vehicle's operation. Predicting the power of new vehicles and finding the similarities between new vehicles and old vehicles was the main goal of this thesis.

Big Data

Big Data Book
Author : Kuan-Ching Li,Hai Jiang,Laurence T. Yang,Alfredo Cuzzocrea
Publisher : CRC Press
Release : 2015-09-15
ISBN : 1498760406
Language : En, Es, Fr & De

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Book Description :

As today’s organizations are capturing exponentially larger amounts of data than ever, now is the time for organizations to rethink how they digest that data. Through advanced algorithms and analytics techniques, organizations can harness this data, discover hidden patterns, and use the newly acquired knowledge to achieve competitive advantages. Presenting the contributions of leading experts in their respective fields, Big Data: Algorithms, Analytics, and Applications bridges the gap between the vastness of Big Data and the appropriate computational methods for scientific and social discovery. It covers fundamental issues about Big Data, including efficient algorithmic methods to process data, better analytical strategies to digest data, and representative applications in diverse fields, such as medicine, science, and engineering. The book is organized into five main sections: Big Data Management—considers the research issues related to the management of Big Data, including indexing and scalability aspects Big Data Processing—addresses the problem of processing Big Data across a wide range of resource-intensive computational settings Big Data Stream Techniques and Algorithms—explores research issues regarding the management and mining of Big Data in streaming environments Big Data Privacy—focuses on models, techniques, and algorithms for preserving Big Data privacy Big Data Applications—illustrates practical applications of Big Data across several domains, including finance, multimedia tools, biometrics, and satellite Big Data processing Overall, the book reports on state-of-the-art studies and achievements in algorithms, analytics, and applications of Big Data. It provides readers with the basis for further efforts in this challenging scientific field that will play a leading role in next-generation database, data warehousing, data mining, and cloud computing research. It also explores related applications in diverse sectors, covering technologies for media/data communication, elastic media/data storage, cross-network media/data fusion, and SaaS.

Big Data

Big Data Book
Author : Min Chen,Shiwen Mao,Yin Zhang,Victor C.M. Leung
Publisher : Springer
Release : 2014-05-05
ISBN : 331906245X
Language : En, Es, Fr & De

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Book Description :

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.

Big Data

Big Data Book
Author : Zongben Xu,Xinbo Gao,Qiguang Miao,Yunquan Zhang,Jiajun Bu
Publisher : Springer
Release : 2018-10-10
ISBN : 9811329222
Language : En, Es, Fr & De

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Book Description :

This volume constitutes the proceedings of the 6th CCF Conference, Big Data 2018, held in Xi'an, China, in October 2018. The 32 revised full papers presented in this volume were carefully reviewed and selected from 880 submissions. The papers are organized in topical sections on natural language processing and text mining; big data analytics and smart computing; big data applications; the application of big data in machine learning; social networks and recommendation systems; parallel computing and storage of big data; data quality control and data governance; big data system and management.

Big Data Management

Big Data Management Book
Author : Peter Ghavami
Publisher : Walter de Gruyter GmbH & Co KG
Release : 2020-11-09
ISBN : 3110664062
Language : En, Es, Fr & De

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Book Description :

Data analytics is core to business and decision making. The rapid increase in data volume, velocity and variety offers both opportunities and challenges. While open source solutions to store big data, like Hadoop, offer platforms for exploring value and insight from big data, they were not originally developed with data security and governance in mind. Big Data Management discusses numerous policies, strategies and recipes for managing big data. It addresses data security, privacy, controls and life cycle management offering modern principles and open source architectures for successful governance of big data. The author has collected best practices from the world’s leading organizations that have successfully implemented big data platforms. The topics discussed cover the entire data management life cycle, data quality, data stewardship, regulatory considerations, data council, architectural and operational models are presented for successful management of big data. The book is a must-read for data scientists, data engineers and corporate leaders who are implementing big data platforms in their organizations.

Big Data

Big Data Book
Author : John Storm Pedersen,Adrian Wilkinson
Publisher : Edward Elgar Publishing
Release : 2019
ISBN : 1788112350
Language : En, Es, Fr & De

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Book Description :

Promise, Application and Pitfalls

Big Data Analytics

Big Data Analytics Book
Author : Anirban Mondal,Himanshu Gupta,Jaideep Srivastava,P. Krishna Reddy,D.V.L.N. Somayajulu
Publisher : Springer
Release : 2018-12-11
ISBN : 3030047806
Language : En, Es, Fr & De

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Book Description :

This book constitutes the refereed proceedings of the 6th International Conference on Big Data analytics, BDA 2018, held in Warangal, India, in December 2018. The 29 papers presented in this volume were carefully reviewed and selected from 93 submissions. The papers are organized in topical sections named: big data analytics: vision and perspectives; financial data analytics and data streams; web and social media data; big data systems and frameworks; predictive analytics in healthcare and agricultural domains; and machine learning and pattern mining.

Big Data Big Analytics

Big Data  Big Analytics Book
Author : Michael Minelli,Michele Chambers,Ambiga Dhiraj
Publisher : John Wiley & Sons
Release : 2012-12-27
ISBN : 1118239156
Language : En, Es, Fr & De

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Book Description :

Unique prospective on the big data analytics phenomenon for both business and IT professionals The availability of Big Data, low-cost commodity hardware and new information management and analytics software has produced a unique moment in the history of business. The convergence of these trends means that we have the capabilities required to analyze astonishing data sets quickly and cost-effectively for the first time in history. These capabilities are neither theoretical nor trivial. They represent a genuine leap forward and a clear opportunity to realize enormous gains in terms of efficiency, productivity, revenue and profitability. The Age of Big Data is here, and these are truly revolutionary times. This timely book looks at cutting-edge companies supporting an exciting new generation of business analytics. Learn more about the trends in big data and how they are impacting the business world (Risk, Marketing, Healthcare, Financial Services, etc.) Explains this new technology and how companies can use them effectively to gather the data that they need and glean critical insights Explores relevant topics such as data privacy, data visualization, unstructured data, crowd sourcing data scientists, cloud computing for big data, and much more.

Big Data

Big Data Book
Author : Rajkumar Buyya,Rodrigo N. Calheiros,Amir Vahid Dastjerdi
Publisher : Morgan Kaufmann
Release : 2016-06-07
ISBN : 0128093463
Language : En, Es, Fr & De

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Book Description :

Big Data: Principles and Paradigms captures the state-of-the-art research on the architectural aspects, technologies, and applications of Big Data. The book identifies potential future directions and technologies that facilitate insight into numerous scientific, business, and consumer applications. To help realize Big Data’s full potential, the book addresses numerous challenges, offering the conceptual and technological solutions for tackling them. These challenges include life-cycle data management, large-scale storage, flexible processing infrastructure, data modeling, scalable machine learning, data analysis algorithms, sampling techniques, and privacy and ethical issues. Covers computational platforms supporting Big Data applications Addresses key principles underlying Big Data computing Examines key developments supporting next generation Big Data platforms Explores the challenges in Big Data computing and ways to overcome them Contains expert contributors from both academia and industry