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Knowledge Graphs and Big Data Processing (Lecture Notes in Computer Science Book 12072)
Categories: Kindle Store > Kindle eBooks > Computers & Technology > Computer Science > Artificial Intelligence > AI & Semantics
ASIN: B08D6WCR1D
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This open access book is part of the LAMBDA Project (Learning, Applying, Multiplying Big Data Analytics), funded by the European Union, GA No. 809965. Data Analytics involves applying algorithmic processes to derive insights. Nowadays it is used in many industries to allow organizations and companies to make better decisions as well as to verify or disprove existing theories or models. The term data analytics is often used interchangeably with intelligence, statistics, reasoning, data mining, knowledge discovery, and others.
The goal of this book is to introduce some of the definitions, methods, tools, frameworks, and solutions for big data processing, starting from the process of information extraction and knowledge representation, via knowledge processing and analytics to visualization, sense-making, and practical applications. Each chapter in this book addresses some pertinent aspect of the data processing chain, with a specific focus on understanding Enterprise Knowledge Graphs, Semantic Big Data Architectures, and Smart Data Analytics solutions.
This book is addressed to graduate students from technical disciplines, to professional audiences following continuous education short courses, and to researchers from diverse areas following self-study courses. Basic skills in computer science, mathematics, and statistics are required.
ASIN : B08D6WCR1D
Publisher : Springer
Accessibility : Learn more
Publication date : July 15, 2020
Edition : 1st ed. 2020
Language : English
File size : 13.7 MB
Screen Reader : Supported
Enhanced typesetting : Enabled
X-Ray : Not Enabled
Word Wise : Not Enabled
Print length : 226 pages
ISBN-13 : 978-3030531997
Page Flip : Enabled
Best Sellers Rank: #35,256 Free in Kindle Store (See Top 100 in Kindle Store) #15 in Software Engineering #40 in Business Software #102 in AI & Semantics
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