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Table of contents

  1. Front Matter
    Pages i-xi
  2. Foundations

    1. Front Matter
      Pages 1-1
    2. Valentina Janev
      Pages 3-19 Open Access
    3. Luigi Bellomarini, Emanuel Sallinger, Sahar Vahdati
      Pages 20-34 Open Access
    4. Hajira Jabeen
      Pages 35-55 Open Access
  3. Architecture

    1. Front Matter
      Pages 57-57
    2. Anastasia Dimou
      Pages 59-72 Open Access
    3. Kemele M. Endris, Maria-Esther Vidal, Damien Graux
      Pages 73-86 Open Access
    4. Luigi Bellomarini, Emanuel Sallinger, Sahar Vahdati
      Pages 87-101 Open Access
  4. Methods and Solutions

    1. Front Matter
      Pages 103-103
    2. Hajira Jabeen, Damien Graux, Gezim Sejdiu
      Pages 105-121 Open Access
    3. Mayesha Tasnim, Diego Collarana, Damien Graux, Maria-Esther Vidal
      Pages 122-146 Open Access
  5. Applications

    1. Front Matter
      Pages 147-147
    2. Valentina Janev, Dea Pujić, Marko Jelić, Maria-Esther Vidal
      Pages 149-164 Open Access
    3. Dea Pujić, Marko Jelić, Nikola Tomašević, Marko Batić
      Pages 165-180 Open Access
  6. Back Matter
    Pages 181-209

About this book

Introduction

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.

Keywords

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

Editors and affiliations

  1. 1.Institute Mihajlo PupinUniversity of BelgradeBelgradeSerbia
  2. 2.ADAPT SFI Centre, O’Reilly InstituteTrinity College DublinDublinIreland
  3. 3.CEPLAS, Botanical InstituteUniversity of CologneCologneGermany
  4. 4.Institute of Logic and Computation, Faculty of InformaticsTU WienWienAustria

Bibliographic information