Knowledge Graphs and Big Data Processing / / edited by Valentina Janev, Damien Graux, Hajira Jabeen, Emanuel Sallinger

• Autore: Janev Valentina
• Pubblicazione: Springer Nature, 2020; Cham : , : Springer International Publishing : , : Imprint : Springer, , 2020
• Edizione: 1st ed. 2020.
• Descrizione fisica: 1 online resource (XI, 209 p. 39 illus., 32 illus. in color.)
• Disciplina: 005.74
• Soggetto topico: Database management; Application software; Artificial intelligence; Computer logic; Management information systems; Database Management; Information Systems Applications (incl. Internet); Logic in AI; Computer Appl. in Administrative Data Processing; Business Information Systems
• Soggetto non controllato: 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
• Persona (resp. second.): JanevValentina; GrauxDamien; JabeenHajira; SallingerEmanuel
• Nota di contenuto: Foundations -- Chapter 1. Ecosystem of Big Data -- Chapter 2. Knowledge Graphs: The Layered Perspective -- Chapter 3. Big Data Outlook, Tools, and Architectures -- Architecture -- Chapter 4. Creation of Knowledge Graphs -- Chapter 5. Federated Query Processing -- Chapter 6. Reasoning in Knowledge Graphs: An Embeddings Spotlight -- Methods and Solutions -- Chapter 7. Scalable Knowledge Graph Processing using SANSA -- Chapter 8. Context-Based Entity Matching for Big Data -- Applications -- Chapter 9. Survey on Big Data Applications -- Chapter 10. Case Study from the Energy Domain.
• Sommario/riassunto: 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.
• Titolo autorizzato: Knowledge Graphs and Big Data Processing
• ISBN: 3-030-53199-6
• Formato: Materiale a stampa
• Livello bibliografico: Monografia
• Lingua di pubblicazione: Inglese
• Record Nr.: 9910413442003321
• Lo trovi qui: Univ. Federico II
• Serie: Information Systems and Applications, incl. Internet/Web, and HCI ; ; 12072