[hpc-announce] Call for Open Access Book Chapters: Technologies and Applications for Big Data Value

[Maria S. Perez]

*Call for Open Access Book Chapters**
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**Title: Technologies and Applications for Big Data Value*

_Editors_
• 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

_Overview_
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.

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

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

_Dates_
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
8th January 2021:          Final Chapter Submission:
Q2 2021:                       Estimated publication

_Submission_
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:
https://easychair.org/conferences/?conf=tabdv2021
For further information, please contact the editors on: 
edward.curry at nuigalway.ie



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María S. Pérez-Hernández

Professor at UPM
http://www.datsi.fi.upm.es/~mperez

E.T.S. Ingenieros Informáticos        Ph.  (+ 34) 910672857
Universidad Politécnica de Madrid     mperez at fi.upm.es
Campus de Montegancedo
28660 Boadilla del Monte (Madrid)
Spain
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