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Titlebook: Knowledge Graphs and Big Data Processing; Valentina Janev,Damien Graux,Emanuel Sallinger Book‘‘‘‘‘‘‘‘ 2020 The Editor(s) (if applicable) a

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發(fā)表于 2025-3-21 19:14:01 | 只看該作者 |倒序?yàn)g覽 |閱讀模式
書(shū)目名稱(chēng)Knowledge Graphs and Big Data Processing
編輯Valentina Janev,Damien Graux,Emanuel Sallinger
視頻videohttp://file.papertrans.cn/544/543938/543938.mp4
概述Studies the potentials, prospects, and challenges of Big Data Analytics in real-world applications.Addresses pertinent aspect of the data processing chain
叢書(shū)名稱(chēng)Lecture Notes in Computer Science
圖書(shū)封面Titlebook: Knowledge Graphs and Big Data Processing; Valentina Janev,Damien Graux,Emanuel Sallinger Book‘‘‘‘‘‘‘‘ 2020 The Editor(s) (if applicable) a
描述.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
出版日期Book‘‘‘‘‘‘‘‘ 2020
關(guān)鍵詞artificial intelligence; big data; data analytics; data handling; data integration; data mining; databases
版次1
doihttps://doi.org/10.1007/978-3-030-53199-7
isbn_softcover978-3-030-53198-0
isbn_ebook978-3-030-53199-7Series ISSN 0302-9743 Series E-ISSN 1611-3349
issn_series 0302-9743
copyrightThe Editor(s) (if applicable) and The Author(s) 2020
The information of publication is updating

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發(fā)表于 2025-3-21 20:46:03 | 只看該作者
Chapter 10 Case Study from the Energy Domainons of big data technologies in the energy domain is clear, and is thus the purpose of this chapter. An application of linked data and post-processing energy data has been covered, whilst a special focus has been put on the analytical services involved, concrete methodologies and their exploitation.
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Book‘‘‘‘‘‘‘‘ 2020. 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 i
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0302-9743 ocessing chain.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 organizatio
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發(fā)表于 2025-3-23 02:54:45 | 只看該作者
Chapter 5 Federated Query Processingery processing techniques provide a solution to scale up?to large volumes of data distributed across multiple data sources. Federated query processing techniques resort to source descriptions to identify relevant data sources for a query, as well as to find efficient execution plans that minimize th
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Chapter 8 Context-Based Entity Matching for Big Data technique that relies on both the knowledge stated in RDF vocabularies and a context-based similarity metric to map contextually equivalent RDF graphs. COMET follows a two-fold approach to solve the problem of entity matching in RDF graphs in a context-aware manner. In the first step, COMET compute
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