Call for Open Access Book Chapters on Technologies and Applications for Big Data Value


The BIG DATA VALUE eCOSYSTEM project has recently launched a call for Open Access Book Chapters for a forthcoming publication on Technologies and Applications for Big Data.  Editors Edward Curry, NUI Galway Sören Auer, Leibniz Universität Hannove...

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The BIG DATA VALUE eCOSYSTEM project has recently launched a call for Open Access Book Chapters for a forthcoming publication on Technologies and Applications for Big Data


  • Edward Curry, NUI Galway
  • Sören Auer, Leibniz Universität Hannover
  • Arne J. Berre, SINTEF
  • Andreas Metzger, University of Duisburg-Essen
  • Maria S. Perez, Universidad Politécnica de Madrid
  • Sonja Zillner, Siemens


The continuous and significant growth of data together with improved access to data and the availability of powerful computing infrastructure have led to intensified activities around Big Data Value and data-driven Artificial Intelligence (AI). Powerful data techniques and tools allow collecting, storing, analysing, processing and visualising vast amounts of data which can enable data-driven disruptive innovation within our work, business, life, industry and society.  The rapidly increasing volumes of diverse data from distributed sources create significant technical challenges for extracting valuable knowledge. Many fundamental, technological and deployment challenges exist in developing and applying big data and data-driven AI to solve real-world problems. For example, what are the technical foundations of data management for data-driven AI? What are key characteristics for efficient and effective data processing architectures for real-time data? How do we deal with trust and quality issues in data analysis and data-driven decision-making? What are the appropriate frameworks for data protection? What is the role of DevOps in delivering scalable solutions? How can big data and data-driven AI be used to power digital transformation in industries?

Aims and Goals

The aim of the book is to educate the reader on how technologies, methods, and processes for big data and data-driven AI can deliver value to address problems in real‐world applications. The book will explore cutting-edge solutions and best practices for big data and data-driven AI, and applications for the data-driven economy. The book provides the reader with a basis for understanding how technical issues can be overcome to provide real-world solutions for major industrial areas, including health, energy, transport, finance, manufacturing, and public administration.

The book is of interest to two primary audiences, first undergraduate, postgraduate students, and researchers in a variety of fields including big data, data science, data engineering, as well as machine learning and AI. The second audience is 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.  

Contributions to this book are in two parts: technologies and methods, and processes and applications as follows.

Part I.  Technologies and Methods

  • Data Management
    • Semantic annotation of large-scale unstructured and semi-structured data
    • Semantic interoperability
    • Data quality and data provenance for large-scale data
    • Data lifecycle management and data governance
    • Integration of large-scale data and business processes
    • Data-as-a-Service
    • Distributed trust infrastructures for data management
  • Data Processing Architectures
    • Real-time support for heterogeneity
    • Scalability and distribution for large-scale data
    • Processing of big data-in-motion and big data-at-rest
    • Decentralisation
    • Performance for large-scale processing
    • Efficient mechanisms for storage and processing of big data
    • Novel architectures for enabling new types of big data workloads (hybrid Big Data and HPC architecture)
    • Hardware-specific capabilities for big data (GPUs, FPGAs)
  • Data Analytics
    • Large-scale semantic and knowledge-based analysis
    • Content validation/veracity
    • Analytics frameworks and processing for big data value
    • Advanced business analytics and intelligence
    • Predictive and prescriptive analytics
    • High Performance Data Analytics (HPDA)
    • Data-driven analytics and Artificial Intelligence
    • Large-scale Event and pattern discovery
    • Large-scale Multimedia (unstructured) data mining
    • Deep learning techniques for business intelligence
  • Data Visualisation and User Interaction
    • Interactive visual analytics of multiple scale data
    • Collaborative, intuitive and interactive visual interfaces for big data value
    • Interactive visual big data exploration
    • Scalable data visualisation approaches and tools
    • Collaborative, 3D and cross-platform big data visualisation frameworks
    • New paradigms for visual data exploration, discovery and querying over large-scale data
    • Personalised end-user-centric reusable data visualisation components for big data value
    • Domain-specific big data visualisation approaches
  • Data Protection
    • Enforceable robust data protection frameworks
    • Privacy-preserving big data mining algorithms.
    • Robust anonymisation algorithms for large-scale data
    • Protection against reversibility in large-scale data
    • Multiparty mining/pattern hiding
  • Development, Deployment and Operations
    • Engineering and DevOps for big data
    • Big Data Value engineering
    • Life-cycle models for data-driven applications
    • Quality assurance for data-driven applications
    • DevOps techniques and tools for data-driven applications

Part II. Processes and Applications
Cases detailing experience reports and lessons of using big data and data-driven approaches in processes and applications. Chapters with (co-)authors from industry are strongly encouraged. Domains of interest include (but not limited to): Energy, Mobility and Logistics, Manufacturing, Retail, Agriculture and Food Production, Health Space, Finance, Smart Cities, Public Administration, Legal and Education.

Schedule and Deadlines

18th September 2020

Chapter proposal submission deadline (abstract only)

2nd October 2020

Notification of proposal acceptance

6th November 2020

Full chapter submission

27th November 2020

Review comments

11th December 2020

Revised Chapter Submission

– Along with response to reviewer comments

18th December 2020

Final acceptance notification

8th January 2021

Final Chapter Submission:

– Along with response to reviewer comments

– Signed Copyright Agreement

Q2 2021

Estimated publication


Researchers and industry practitioners are invited to submit on or before September 18, 2020, a brief summary (abstract) consisting of a title and 150-200 words clearly identifying the main objectives of your contribution and how it fits within the edited book. Industrial (co-)authors are particularly encouraged to report on their experiences and lessons learnt in Part II. Authors of accepted proposals will be notified in October 2020 about the status of their proposals and provided chapter formatting guidelines.  

Chapter proposal must be submitted via EasyChair at:

For further information, please contact the editors on: