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Knowledge graphs and big data processing / Valentina Janev (2020)  

Public Titre : Knowledge graphs and big data processing Type de document : Monographie Auteurs : Valentina Janev, Éditeur scientifique ; Damien Graux, Éditeur scientifique ; Hajira Jabeen, Éditeur scientifique ; Emanuel Sallinger, Éditeur scientifique Editeur : Berlin, Heidelberg, Vienne, New York, ... : Springer Année de publication : 2020 Importance : 307 p. Format : 17 x 25 cm ISBN/ISSN/EAN : 978-3-030-53199-7 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Systèmes d'information [Termes IGN] données d'entrainement (apprentissage automatique) [Termes IGN] données massives [Termes IGN] écosystème [Termes IGN] énergie [Termes IGN] exploration de données [Termes IGN] ingénierie des connaissances [Termes IGN] intelligence artificielle [Termes IGN] réseau sémantique [Termes IGN] segmentation sémantique [Termes IGN] système à base de connaissances Résumé : (éditeur) 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. Note de contenu : 1- Ecosystem of big data 2- Knowledge graphs: The layered perspective 3- Big data outlook, tools, and architectures 4- Creation of knowledge graphs 5- Federated query processing 6- Reasoning in knowledge graphs: An embeddings spotlight 7- Scalable knowledge graph processing using SANSA 8- Context-based entity matching for big data 9- Survey on big data applications 10- Case study from the energy domain 11- Numéro de notice : 25928 Affiliation des auteurs : non IGN Thématique : INFORMATIQUE/SOCIETE NUMERIQUE Nature : Monographie DOI : 10.1007/978-3-030-53199-7 En ligne : https://doi.org/10.1007/978-3-030-53199-7 Format de la ressource électronique : URL Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=96189