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Open Source Data Warehousing and Business Intelligence, Bulusu, Lakshman


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Цена: 9033.00р.
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При оформлении заказа до: 2025-08-18
Ориентировочная дата поставки: конец Сентября - начало Октября
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Автор: Bulusu, Lakshman
Название:  Open Source Data Warehousing and Business Intelligence
ISBN: 9781138374225
Издательство: Taylor&Francis
Классификация:


ISBN-10: 1138374229
Обложка/Формат: Paperback
Страницы: 428
Вес: 0.79 кг.
Дата издания: 30.08.2019
Язык: English
Иллюстрации: 7 tables, black and white; 30 illustrations, black and white
Размер: 235 x 159
Читательская аудитория: Tertiary education (us: college)
Основная тема: Software Engineering & Systems Development
Ссылка на Издательство: Link
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Поставляется из: Европейский союз
Описание:

Open Source Data Warehousing and Business Intelligence is an all-in-one reference for developing open source based data warehousing (DW) and business intelligence (BI) solutions that are business-centric, cross-customer viable, cross-functional, cross-technology based, and enterprise-wide. Considering the entire lifecycle of an open source DW & BI implementation, its comprehensive coverage spans from basic concepts all the way through to customization.

Highlighting the key differences between open source and vendor DW and BI technologies, the book identifies end-to-end solutions that are scalable, high performance, and stable. It illustrates the practical aspects of implementing and using open source DW and BI technologies to supply you with valuable on-the-project experience that can help you improve implementation and productivity.

Emphasizing analysis, design, and programming, the text explains best-fit solutions as well as how to maximize ROI. Coverage includes data warehouse design, real-time processing, data integration, presentation services, and real-time reporting. With a focus on real-world applications, the author devotes an entire section to powerful implementation best practices that can help you build customer confidence while saving valuable time, effort, and resources.




Computing with Data

Автор: Guy Lebanon; Mohamed El-Geish
Название: Computing with Data
ISBN: 331998148X ISBN-13(EAN): 9783319981482
Издательство: Springer
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Цена: 13974.00 р.
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Описание: This book introduces basic computing skills designed for industry professionals without a strong computer science background. Written in an easily accessible manner, and accompanied by a user-friendly website, it serves as a self-study guide to survey data science and data engineering for those who aspire to start a computing career, or expand on their current roles, in areas such as applied statistics, big data, machine learning, data mining, and informatics.The authors draw from their combined experience working at software and social network companies, on big data products at several major online retailers, as well as their experience building big data systems for an AI startup. Spanning from the basic inner workings of a computer to advanced data manipulation techniques, this book opens doors for readers to quickly explore and enhance their computing knowledge.Computing with Data comprises a wide range of computational topics essential for data scientists, analysts, and engineers, providing them with the necessary tools to be successful in any role that involves computing with data. The introduction is self-contained, and chapters progress from basic hardware concepts to operating systems, programming languages, graphing and processing data, testing and programming tools, big data frameworks, and cloud computing.The book is fashioned with several audiences in mind. Readers without a strong educational background in CS--or those who need a refresher--will find the chapters on hardware, operating systems, and programming languages particularly useful. Readers with a strong educational background in CS, but without significant industry background, will find the following chapters especially beneficial: learning R, testing, programming, visualizing and processing data in Python and R, system design for big data, data stores, and software craftsmanship.

Free and Open Source Software in Modern Data Science and Business Intelligence: Emerging Research and Opportunities

Автор: K.G. Srinivasa, Ganesh Chandra Deka, Krishna Raj P.M.
Название: Free and Open Source Software in Modern Data Science and Business Intelligence: Emerging Research and Opportunities
ISBN: 1522537074 ISBN-13(EAN): 9781522537076
Издательство: Mare Nostrum (Eurospan)
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Цена: 27720.00 р.
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Описание: Examines the differences between the two types of software, integral in the FOSS movement, and their effect on the distribution and use of software. Featuring coverage on a wide range of topics, such as FOSS Ecology, graph mining, and project tasks, this book is geared towards academics, researchers, and students interested in current research on the growing importance of FOSS.

The Art of Feature Engineering: Essentials for Machine Learning

Автор: Pablo Duboue
Название: The Art of Feature Engineering: Essentials for Machine Learning
ISBN: 1108709389 ISBN-13(EAN): 9781108709385
Издательство: Cambridge Academ
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Цена: 6970.00 р.
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Описание: This is a guide for data scientists who want to use feature engineering to improve the performance of their machine learning solutions. The book provides a unified view of the field, beginning with basic concepts and techniques, followed by a cross-domain approach to advanced topics, like texts and images, with hands-on case studies.

Cyberspace Data and Intelligence, and Cyber-Living, Syndrome, and Health

Автор: Huansheng Ning
Название: Cyberspace Data and Intelligence, and Cyber-Living, Syndrome, and Health
ISBN: 9811519218 ISBN-13(EAN): 9789811519215
Издательство: Springer
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Цена: 6986.00 р.
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Описание: This two-volume set (CCIS 1137 and CCIS 1138) constitutes the proceedings of the Third International Conference on Cyberspace Data and Intelligence, Cyber DI 2019, and the International Conference on Cyber-Living, Cyber-Syndrome, and Cyber-Health, CyberLife 2019, held under the umbrella of the 2019 Cyberspace Congress, held in Beijing, China, in December 2019.The 64 full papers presented together with 18 short papers were carefully reviewed and selected from 160 submissions. The papers are grouped in the following topics: Cyber Data, Information and Knowledge; Cyber and Cyber-enabled Intelligence; Communication and Computing; Cyber Philosophy, Cyberlogic and Cyber Science; and Cyber Health and Smart Healthcare.

Artificial Intelligence for Fashion Industry in the Big Data Era

Автор: Thomassey
Название: Artificial Intelligence for Fashion Industry in the Big Data Era
ISBN: 9811300798 ISBN-13(EAN): 9789811300790
Издательство: Springer
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Цена: 27950.00 р.
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Описание:

This book provides an overview of current issues and challenges in the fashion industry and an update on data-driven artificial intelligence (AI) techniques and their potential implementation in response to those challenges. Each chapter starts off with an example of a data-driven AI technique on a particular sector of the fashion industry (design, manufacturing, supply or retailing), before moving on to illustrate its implementation in a real-world application
Agent Intelligence Through Data Mining

Автор: Andreas L. Symeonidis; Pericles A. Mitkas
Название: Agent Intelligence Through Data Mining
ISBN: 1441937242 ISBN-13(EAN): 9781441937247
Издательство: Springer
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Цена: 19564.00 р.
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Описание: This book addresses the use of data mining for smarter, more efficient agents, as well as the challenge of generating intelligence from data while transferring it to a separate, possibly autonomous, software entity.

Intelligence Science and Big Data Engineering. Big Data and Machine Learning

Автор: Zhen Cui; Jinshan Pan; Shanshan Zhang; Liang Xiao;
Название: Intelligence Science and Big Data Engineering. Big Data and Machine Learning
ISBN: 3030362035 ISBN-13(EAN): 9783030362034
Издательство: Springer
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Цена: 6986.00 р.
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Описание: The two volumes LNCS 11935 and 11936 constitute the proceedings of the 9th International Conference on Intelligence Science and Big Data Engineering, IScIDE 2019, held in Nanjing, China, in October 2019.The 84 full papers presented were carefully reviewed and selected from 252 submissions.The papers are organized in two parts: visual data engineering; and big data and machine learning. They cover a large range of topics including information theoretic and Bayesian approaches, probabilistic graphical models, big data analysis, neural networks and neuro-informatics, bioinformatics, computational biology and brain-computer interfaces, as well as advances in fundamental pattern recognition techniques relevant to image processing, computer vision and machine learning.

Getting Started with Geo, Couchdb, and Node.Js: New Open Source Tools for Location Data

Автор: Thompson Mick
Название: Getting Started with Geo, Couchdb, and Node.Js: New Open Source Tools for Location Data
ISBN: 1449307523 ISBN-13(EAN): 9781449307523
Издательство: Wiley
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Цена: 3166.00 р.
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Описание: Today`s mobile devices have GPS and standard APIs to give you access to coordinates - but what can you do with that data? With this concise book, application developers learn how to work with location data quickly and easily, using Node.js, CouchDB, and other open source tools and libraries.

Open source software for statistical analysis of big data

Название: Open source software for statistical analysis of big data
ISBN: 1799827690 ISBN-13(EAN): 9781799827696
Издательство: Mare Nostrum (Eurospan)
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Цена: 22869.00 р.
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Описание: Presents research exploring the theoretical and practical aspects of cost-free software possibilities for applications within data analysis and statistics with a specific focus on R and Python. The book features coverage on a broad range of topics, such as cluster analysis, time series forecasting, and machine learning.

Open source software for statistical analysis of big data

Название: Open source software for statistical analysis of big data
ISBN: 1799827682 ISBN-13(EAN): 9781799827689
Издательство: Mare Nostrum (Eurospan)
Рейтинг:
Цена: 30215.00 р.
Наличие на складе: Есть у поставщика Поставка под заказ.

Описание: Presents research exploring the theoretical and practical aspects of cost-free software possibilities for applications within data analysis and statistics with a specific focus on R and Python. The book features coverage on a broad range of topics, including cluster analysis, time series forecasting, and machine learning.


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