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    Knowledge Graphs and Big Data Processing

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    Contributor(s)
    Janev, Valentina (editor)
    Graux, Damien (editor)
    Jabeen, Hajira (editor)
    Sallinger, Emanuel (editor)
    Collection
    European Research Council (ERC); EU collection
    Language
    English
    Show full item record
    Abstract
    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.
    URI
    https://library.oapen.org/handle/20.500.12657/41294
    Keywords
    Database Management; Information Systems Applications (incl. Internet); Logic in AI; Computer Appl. in Administrative Data Processing; Business Information Systems; Computer and Information Systems Applications; Computer Application in Administrative Data Processing; artificial intelligence; big data; data analytics; data handling; data integration; data mining; databases; digital storage; domain knowledge; graph theory; information management; information technology; integrated data; internet; knowledge management; knowledge-based system; ontologies; semantics; Databases; Database programming; Information retrieval; Internet searching; Artificial intelligence; Public administration; Information technology: general issues; Business mathematics & systems
    DOI
    10.1007/978-3-030-53199-7
    Publisher
    Springer Nature
    Publisher website
    https://www.springernature.com/gp/products/books
    Publication date and place
    2020
    Grantor
    • H2020 European Research Council
    Imprint
    Springer
    Series
    Lecture Notes in Computer Science; Information Systems and Applications, incl. Internet/Web, and HCI, 12072
    Classification
    Databases
    Information retrieval
    Artificial intelligence
    Public administration
    Business mathematics and systems
    Pages
    209
    Rights
    http://creativecommons.org/licenses/by/4.0/
    • Imported or submitted locally

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    License

    • If not noted otherwise all contents are available under Attribution 4.0 International (CC BY 4.0)

    Credits

    • logo EU
    • This project received funding from the European Union's Horizon 2020 research and innovation programme under grant agreement No 683680, 810640, 871069 and 964352.

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