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  • Book
  • Open Access
  • © 2022

Technologies and Applications for Big Data Value

Editors:

  1. Edward Curry

    1. Insight SFI Research Centre for Data Analytics, NUI Galway, Ireland

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  2. Sören Auer

    1. Information Centre for Science and Technology, Leibniz University Hannover, Hannover, Germany

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  3. Arne J. Berre

    1. SINTEF Digital, Oslo, Norway

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  4. Andreas Metzger

    1. Paluno, University of Duisburg-Essen, Essen, Germany

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  5. Maria S. Perez

    1. Universidad Politécnica de Madrid, Boadilla del Monte, Madrid, Spain

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  6. Sonja Zillner

    1. Siemens Corporate Technology, München, Germany

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  • Explores cutting-edge solutions and best practices for big data and data-driven AI applications for the data-driven economy

  • Provides an overview of technologies and methods which enable data value chains and which can be applied in any sector

  • Details experience reports and lessons from using big data and data-driven approaches in processes and applications from key application domains including health, law, finance, retail, manufacturing, mobility, transport, and smart cities

  • This book is open access, which means that you have free and unlimited access

Sections

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Table of contents (23 chapters)

  1. Front Matter

    Pages i-xxiv
    PDF

  2. Technologies and Applications for Big Data Value

    • Edward Curry, Sören Auer, Arne J. Berre, Andreas Metzger, Maria S. Perez, Sonja Zillner
    Pages 1-15Open Access

  3. Technologies and Methods

    1. Front Matter

      Pages 17-17
      PDF

    2. Supporting Semantic Data Enrichment at Scale

      • Michele Ciavotta, Vincenzo Cutrona, Flavio De Paoli, Nikolay Nikolov, Matteo Palmonari, Dumitru Roman
      Pages 19-39Open Access

    3. Trade-Offs and Challenges of Serverless Data Analytics

      • Pedro García-López, Marc Sánchez-Artigas, Simon Shillaker, Peter Pietzuch, David Breitgand, Gil Vernik et al.
      Pages 41-61Open Access

    4. Big Data and AI Pipeline Framework: Technology Analysis from a Benchmarking Perspective

      • Arne J. Berre, Aphrodite Tsalgatidou, Chiara Francalanci, Todor Ivanov, Tomas Pariente-Lobo, Ricardo Ruiz-Saiz et al.
      Pages 63-88Open Access

    5. An Elastic Software Architecture for Extreme-Scale Big Data Analytics

      • Maria A. Serrano, César A. Marín, Anna Queralt, Cristovao Cordeiro, Marco Gonzalez, Luis Miguel Pinho et al.
      Pages 89-110Open Access

    6. Privacy-Preserving Technologies for Trusted Data Spaces

      • Susanna Bonura, Davide Dalle Carbonare, Roberto Díaz-Morales, Marcos Fernández-Díaz, Lucrezia Morabito, Luis Muñoz-González et al.
      Pages 111-134Open Access

    7. Leveraging Data-Driven Infrastructure Management to Facilitate AIOps for Big Data Applications and Operations

      • Richard McCreadie, John Soldatos, Jonathan Fuerst, Mauricio Fadel Argerich, George Kousiouris, Jean-Didier Totow et al.
      Pages 135-158Open Access

    8. Leveraging High-Performance Computing and Cloud Computing with Unified Big-Data Workflows: The LEXIS Project

      • Stephan Hachinger, Martin Golasowski, Jan Martinovič, Mohamad Hayek, Rubén Jesús García-Hernández, Kateřina Slaninová et al.
      Pages 159-180Open Access

  4. Processes and Applications

    1. Front Matter

      Pages 181-181
      PDF

    2. The DeepHealth Toolkit: A Key European Free and Open-Source Software for Deep Learning and Computer Vision Ready to Exploit Heterogeneous HPC and Cloud Architectures

      • Marco Aldinucci, David Atienza, Federico Bolelli, Mónica Caballero, Iacopo Colonnelli, José Flich et al.
      Pages 183-202Open Access

    3. Applying AI to Manage Acute and Chronic Clinical Condition

      • Rachael Hagan, Charles J. Gillan, Murali Shyamsundar
      Pages 203-223Open Access

    4. 3D Human Big Data Exchange Between the Healthcare and Garment Sectors

      • Juan V. Durá Gil, Alfredo Remon, Iván Martínez Rodriguez, Tomas Pariente-Lobo, Sergio Salmeron-Majadas, Antonio Perrone et al.
      Pages 225-252Open Access

    5. Using a Legal Knowledge Graph for Multilingual Compliance Services in Labor Law, Contract Management, and Geothermal Energy

      • Martin Kaltenboeck, Pascual Boil, Pieter Verhoeven, Christian Sageder, Elena Montiel-Ponsoda, Pablo Calleja-Ibáñez
      Pages 253-271Open Access

    6. Big Data Analytics in the Banking Sector: Guidelines and Lessons Learned from the CaixaBank Case

      • Andreas Alexopoulos, Yolanda Becerra, Omer Boehm, George Bravos, Vasilis Chatzigiannakis, Cesare Cugnasco et al.
      Pages 273-297Open Access

    8. Big Data Analytics in the Manufacturing Sector: Guidelines and Lessons Learned Through the Centro Ricerche FIAT (CRF) Case

      • Andreas Alexopoulos, Yolanda Becerra, Omer Boehm, George Bravos, Vassilis Chatzigiannakis, Cesare Cugnasco et al.
      Pages 321-344Open Access

    9. Next-Generation Big Data-Driven Factory 4.0 Operations and Optimization: The Boost 4.0 Experience

      • Oscar Lázaro, Jesús Alonso, Philip Ohlsson, Bas Tijsma, Dominika Lekse, Bruno Volckaert et al.
      Pages 345-371Open Access

    10. Big Data-Driven Industry 4.0 Service Engineering Large-Scale Trials: The Boost 4.0 Experience

      • Oscar Lázaro, Jesús Alonso, Paulo Figueiras, Ruben Costa, Diogo Graça, Gisela Garcia et al.
      Pages 373-397Open Access
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About this book

This open access book explores cutting-edge solutions and best practices for big data and data-driven AI applications for the data-driven economy. It provides the reader with a basis for understanding how technical issues can be overcome to offer real-world solutions to major industrial areas.

The book starts with an introductory chapter that provides an overview of the book by positioning the following chapters in terms of their contributions to technology frameworks which are key elements of the Big Data Value Public-Private Partnership and the upcoming Partnership on AI, Data and Robotics. The remainder of the book is then arranged in two parts. The first part “Technologies and Methods” contains horizontal contributions of technologies and methods that enable data value chains to be applied in any sector. The second part “Processes and Applications” details experience reports and lessons from using big data and data-driven approaches in processes and applications. Its chapters are co-authored with industry experts and cover domains including health, law, finance, retail, manufacturing, mobility, and smart cities. Contributions emanate from the Big Data Value Public-Private Partnership and the Big Data Value Association, which have acted as the European data community's nucleus to bring together businesses with leading researchers to harness the value of data to benefit society, business, science, and industry.

The book is of interest to two primary audiences, first, undergraduate and postgraduate students and researchers in various fields, including big data, data science, data engineering, and machine learning and AI. Second, practitioners and industry experts engaged in data-driven systems, software design and deployment projects who are interested in employing these advanced methods to address real-world problems.

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Keywords

  • Big Data
  • Data Management
  • Data Processing
  • Data Analytics
  • Data Visualisation and User Interaction
  • Knowledge Discovery
  • Information Retrieval
  • Open Access
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Editors and Affiliations

  • Insight SFI Research Centre for Data Analytics, NUI Galway, Ireland

    Edward Curry

  • Information Centre for Science and Technology, Leibniz University Hannover, Hannover, Germany

    Sören Auer

  • SINTEF Digital, Oslo, Norway

    Arne J. Berre

  • Paluno, University of Duisburg-Essen, Essen, Germany

    Andreas Metzger

  • Universidad Politécnica de Madrid, Boadilla del Monte, Madrid, Spain

    Maria S. Perez

  • Siemens Corporate Technology, München, Germany

    Sonja Zillner

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About the editors

Edward Curry is a research leader at the Insight SFI Research Centre for Data Analytics. He has made contributions to semantic technologies, incremental data management, event processing middleware, software engineering, and distributed systems and information systems. Edward combines strong theoretical results with high-impact practical applications. He is also co-founder and elected Vice President of the Big Data Value Association, an industry-led European big data community.

Sören Auer is Professor of Data Science and Digital Libraries at Leibniz Universität Hannover and Director of the TIB, the largest science and technology library in the world. He has made important contributions to semantic technologies, knowledge engineering and information systems. He is co-founder of several high potential research and community projects such as the Wikipedia semantification project DBpedia, the scholarly platform knowledge graphorkg.org and the innovative technology start-up eccenca.com. Sören also was founding director of the Big Data Value Association, led the semantic data representation in the International Data Space, and is an expert for industry, the European Commission and W3C.

Arne J. Berre  is Chief Scientist at SINTEF Digital and Innovation Director at the Norwegian Center for AI Innovation (NorwAI), responsible for the GEMINI center of Big Data and AI. He is the leader of the BDVA/DAIRO TF6 on technical priorities including responsibilities for data technology architectures, data science/AI, data protection, standardisation, benchmarking and HPC, as well as the lead of the Norwegian committee for AI and Big Data with ISO SC 42 AI.

Andreas Metzger is senior academic councillor at the University of Duisburg-Essen and heads the Adaptive Systems and Big Data Applications group at paluno, the Ruhr Institute for Software Technology. His background and research interests are software engineering and machine learning for adaptive systems. Among other leadership roles, Andreas acted as Technical Coordinator of the European lighthouse project TransformingTransport, which demonstrated the transformations that big data and machine learning can bring to the mobility and logistics sector.

Maria S. Perez is full professor at the Universidad Politécnica de Madrid (UPM). She is part of the Board of Directors of the Big Data Value Association and also a member of the Research and Innovation Advisory Group of the EuroHPC Joint Undertaking. Her research interests include data science, big data, machine learning, storage, high performance, and large-scale computing.

Sonja Zillner works at Siemens AG Technology as Principal Research Scientist, focusing on the definition, acquisition and management of global innovation and research projects in the domain of semantics and artificial intelligence.  Since 2020 she is Lead of Core Company Technology Module “Trustworthy AI” at Siemens Corporate Technology. Before that, from 2016 to 2019 she was invited to consult the Siemens Advisory Board in strategic decisions regarding artificial intelligence. In addition, Sonja is professor at Technical University in Munich
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Bibliographic Information

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Softcover Book USD 49.99
Price excludes VAT (USA)
Hardcover Book USD 59.99
Price excludes VAT (USA)
Learn about institutional subscriptions