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Principles of Big Data

Principles of Big Data
  • Author : Jules J. Berman
  • Publsiher : Newnes
  • Release : 20 May 2013
  • ISBN : 0124047246
  • Pages : 288 pages
  • Rating : 4/5 from 21 ratings
GET THIS BOOKPrinciples of Big Data

Summary:
Principles of Big Data helps readers avoid the common mistakes that endanger all Big Data projects. By stressing simple, fundamental concepts, this book teaches readers how to organize large volumes of complex data, and how to achieve data permanence when the content of the data is constantly changing. General methods for data verification and validation, as specifically applied to Big Data resources, are stressed throughout the book. The book demonstrates how adept analysts can find relationships among data objects held in disparate Big Data resources, when the data objects are endowed with semantic support (i.e., organized in classes of uniquely identified data objects). Readers will learn how their data can be integrated with data from other resources, and how the data extracted from Big Data resources can be used for purposes beyond those imagined by the data creators. Learn general methods for specifying Big Data in a way that is understandable to humans and to computers Avoid the pitfalls in Big Data design and analysis Understand how to create and use Big Data safely and responsibly with a set of laws, regulations and ethical standards that apply to the acquisition, distribution and integration of Big Data resources


Principles of Big Data

Principles of Big Data
  • Author : Jules J. Berman
  • Publisher : Newnes
  • Release : 20 May 2013
GET THIS BOOKPrinciples of Big Data

Principles of Big Data helps readers avoid the common mistakes that endanger all Big Data projects. By stressing simple, fundamental concepts, this book teaches readers how to organize large volumes of complex data, and how to achieve data permanence when the content of the data is constantly changing. General methods for data verification and validation, as specifically applied to Big Data resources, are stressed throughout the book. The book demonstrates how adept analysts can find relationships among data objects held


Principles and Practice of Big Data

Principles and Practice of Big Data
  • Author : Jules J Berman
  • Publisher : Academic Press
  • Release : 23 July 2018
GET THIS BOOKPrinciples and Practice of Big Data

Principles and Practice of Big Data: Preparing, Sharing, and Analyzing Complex Information, Second Edition updates and expands on the first edition, bringing a set of techniques and algorithms that are tailored to Big Data projects. The book stresses the point that most data analyses conducted on large, complex data sets can be achieved without the use of specialized suites of software (e.g., Hadoop), and without expensive hardware (e.g., supercomputers). The core of every algorithm described in the book


Big Data

Big Data
  • Author : Nathan Marz,James Warren
  • Publisher : Manning Publications Company
  • Release : 03 March 2021
GET THIS BOOKBig Data

Summary Big Data teaches you to build big data systems using an architecture that takes advantage of clustered hardware along with new tools designed specifically to capture and analyze web-scale data. It describes a scalable, easy-to-understand approach to big data systems that can be built and run by a small team. Following a realistic example, this book guides readers through the theory of big data systems, how to implement them in practice, and how to deploy and operate them once


Principles and Practice of Big Data, 2nd Edition

Principles and Practice of Big Data, 2nd Edition
  • Author : Jules Berman
  • Publisher : Anonim
  • Release : 03 March 2021
GET THIS BOOKPrinciples and Practice of Big Data, 2nd Edition

Principles and Practice of Big Data: Preparing, Sharing, and Analyzing Complex Information, Second Edition updates and expands on the first edition, bringing a set of techniques and algorithms that are tailored to Big Data projects. The book stresses the point that most data analyses conducted on large, complex data sets can be achieved without the use of specialized suites of software (e.g., Hadoop), and without expensive hardware (e.g., supercomputers). The core of every algorithm described in the book


Big Data

Big Data
  • Author : Rajkumar Buyya,Rodrigo N. Calheiros,Amir Vahid Dastjerdi
  • Publisher : Morgan Kaufmann
  • Release : 07 June 2016
GET THIS BOOKBig Data

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,


Data Privacy

Data Privacy
  • Author : Nataraj Venkataramanan,Ashwin Shriram
  • Publisher : CRC Press
  • Release : 03 October 2016
GET THIS BOOKData Privacy

The book covers data privacy in depth with respect to data mining, test data management, synthetic data generation etc. It formalizes principles of data privacy that are essential for good anonymization design based on the data format and discipline. The principles outline best practices and reflect on the conflicting relationship between privacy and utility. From a practice standpoint, it provides practitioners and researchers with a definitive guide to approach anonymization of various data formats, including multidimensional, longitudinal, time-series, transaction, and


The Politics and Policies of Big Data

The Politics and Policies of Big Data
  • Author : Ann Rudinow Sætnan,Ingrid Schneider,Nicola Green
  • Publisher : Routledge
  • Release : 08 May 2018
GET THIS BOOKThe Politics and Policies of Big Data

Big Data, gathered together and re-analysed, can be used to form endless variations of our persons - so-called ‘data doubles’. Whilst never a precise portrayal of who we are, they unarguably contain glimpses of details about us that, when deployed into various routines (such as management, policing and advertising) can affect us in many ways. How are we to deal with Big Data? When is it beneficial to us? When is it harmful? How might we regulate it? Offering careful


Data Visualization

Data Visualization
  • Author : Alexandru C. Telea
  • Publisher : CRC Press
  • Release : 18 September 2014
GET THIS BOOKData Visualization

Designing a complete visualization system involves many subtle decisions. When designing a complex, real-world visualization system, such decisions involve many types of constraints, such as performance, platform (in)dependence, available programming languages and styles, user-interface toolkits, input/output data format constraints, integration with third-party code, and more. Focusing on those techniques and methods with the broadest applicability across fields, the second edition of Data Visualization: Principles and Practice provides a streamlined introduction to various visualization techniques. The book illustrates a


Principles of Managerial Statistics and Data Science

Principles of Managerial Statistics and Data Science
  • Author : Roberto Rivera
  • Publisher : John Wiley & Sons
  • Release : 19 February 2020
GET THIS BOOKPrinciples of Managerial Statistics and Data Science

Introduces readers to the principles of managerial statistics and data science, with an emphasis on statistical literacy of business students Through a statistical perspective, this book introduces readers to the topic of data science, including Big Data, data analytics, and data wrangling. Chapters include multiple examples showing the application of the theoretical aspects presented. It features practice problems designed to ensure that readers understand the concepts and can apply them using real data. Over 100 open data sets used for examples



Principles and Methods for Data Science

Principles and Methods for Data Science
  • Author : Anonim
  • Publisher : Elsevier
  • Release : 28 May 2020
GET THIS BOOKPrinciples and Methods for Data Science

Principles and Methods for Data Science, Volume 43 in the Handbook of Statistics series, highlights new advances in the field, with this updated volume presenting interesting and timely topics, including Competing risks, aims and methods, Data analysis and mining of microbial community dynamics, Support Vector Machines, a robust prediction method with applications in bioinformatics, Bayesian Model Selection for Data with High Dimension, High dimensional statistical inference: theoretical development to data analytics, Big data challenges in genomics, Analysis of microarray gene expression



Translation Quality Assessment

Translation Quality Assessment
  • Author : Joss Moorkens,Sheila Castilho,Federico Gaspari,Stephen Doherty
  • Publisher : Springer
  • Release : 13 July 2018
GET THIS BOOKTranslation Quality Assessment

This is the first volume that brings together research and practice from academic and industry settings and a combination of human and machine translation evaluation. Its comprehensive collection of papers by leading experts in human and machine translation quality and evaluation who situate current developments and chart future trends fills a clear gap in the literature. This is critical to the successful integration of translation technologies in the industry today, where the lines between human and machine are becoming increasingly


Data Science and Big Data Analytics

Data Science and Big Data Analytics
  • Author : EMC Education Services
  • Publisher : John Wiley & Sons
  • Release : 05 January 2015
GET THIS BOOKData Science and Big Data Analytics

Data Science and Big Data Analytics is about harnessing the power of data for new insights. The book covers the breadth of activities and methods and tools that Data Scientists use. The content focuses on concepts, principles and practical applications that are applicable to any industry and technology environment, and the learning is supported and explained with examples that you can replicate using open-source software. This book will help you: Become a contributor on a data science team Deploy a


Big Data-Enabled Nursing

Big Data-Enabled Nursing
  • Author : Connie W. Delaney,Charlotte A. Weaver,Judith J. Warren,Thomas R. Clancy,Roy L. Simpson
  • Publisher : Springer
  • Release : 02 November 2017
GET THIS BOOKBig Data-Enabled Nursing

Historically, nursing, in all of its missions of research/scholarship, education and practice, has not had access to large patient databases. Nursing consequently adopted qualitative methodologies with small sample sizes, clinical trials and lab research. Historically, large data methods were limited to traditional biostatical analyses. In the United States, large payer data has been amassed and structures/organizations have been created to welcome scientists to explore these large data to advance knowledge discovery. Health systems electronic health records (EHRs) have