FYI, the uncertainty aspect may be of interest to many of us, as well as other aspects
From: Shyi-Ming Chen <smchen@mail.ntust.edu.tw>
Sent: Friday, April 30, 2021 10:19 AM
Could you help
to send the
CFP of Book Chapters of our edited volume entitled “Recent
Advancements in Multi-View
Data Analytics” to your colleagues to
be published by
Springer-Verlag (Please
see the enclosed file).
Thank you very much.
Looking forward to hearing from you.
Cordially,
Witold Pedrycz and Shyi-Ming Chen
CALL FOR CHAPTERS
Recent Advancements in Multi-View
Data Analytics
To be published by
Springer-Verlag
Witold Pedrycz and Shyi-Ming Chen (Editors)
In real-world problems, there is a visible shift in the realm of data analytics: more often we witness data originating from a number of sources, on a basis of which models are to be constructed.
Data are generated by numerous locally available sensors distributed across some geographically distant areas. Data come from numerous local databases. All of them provide a valuable multi-view perspective at real-world phenomena. Each of these views provides
an essential contribution to the overall understanding of the entire system under analysis. Quite commonly data might not be shared because of some existing technical or legal requirements. Because of the existing constraints, there arises a timely need for
establishing innovative ways of data processing that can be jointly referred to as a multi-view data analytics. A multi-view character of problems is manifested in a variety of scenarios including clustering, consensus building in decision-processes, computer
vision, knowledge representation, big data, data streaming, among others. Given the existing constraints, the aim is to analyze and design processes and algorithms of data analytics addressing the specificity of this class of problems and the inherent structure
of the data. Given the diversity of perspectives encountered in the problem at hand, it becomes imperative to develop efficient and interpretable ways of assessing the performance of results produced by multi-view analytics.
The objective of the proposed volume is to provide the reader with a comprehensive and up-to-date treatise of the area of multi-view data analytics
by focusing a spectrum of methodological and algorithmic issues, discussing implementations and case studies, identifying the best design practices, assessing business models and practices of the methodology of this data analytics as encountered nowadays in
industry, health care, science, administration, and business.
Topics of interest include, but are not limited to, the following:
I.
Multi-View
Aspects of Data Analytics–
An
Overview
Conceptual and methodological platform for building, analyzing, and deploying multi-view architectures. Historical overview.
The concepts of sources of data and knowledge.
Terminology and existing taxonomy.
Performance measures.
II. The
Principles and
Methodology of
Multi-View
Algorithms
Main mechanisms:
Emergence and a historical perspective.
Main problems and their formulations.
Representative studies. Analysis of case studies.
Technical challenges.
III.
Multi-View
Clustering and
Multi-View
Models
Key methodologies and algorithms.
Clustering and clustering with collections of data.
Analysis and reconciliation of clustering results.
Mechanisms of collaboration.
Federated learning.
Performance and scalability analysis.
IV.
Uncertainty and Information
Granularity in
Multi-View
Data
Analytics
Quality and diversity of data sources.
Conflicting views and consensus building processes.
Granular quantification of results.
V. Case
Studies and
Applications
A collection of selected case studies reported in various areas of engineering, health, business, and science, in particular transportation, healthcare, finance, military, and legal.
Submission Procedure
Potential authors are invited to submit a brief
one-page
summary (including the affiliations, the names and the E-mail addresses of the authors)
of the proposed chapter clearly identifying the main objectives of their research
before May
20,
2021. Authors
of the accepted proposals will be notified and provided with detailed guidelines.
Full chapters are to be submitted by
July 1, 2021.
All manuscripts will be thoroughly reviewed. All
corresponding authors will receive an email from Springer explaining how to access the ebook.
The proposals and manuscripts are to be submitted electronically to both editors (wpedrycz@ualberta.ca
and smchen@mail.ntust.edu.tw).
Important Dates
Brief Proposal Submission
May 20, 2021
Notification of Acceptance
May
31, 2021
Full Chapter Submission
July 1, 2021
Review Results Returned
August 15, 2021
Final Chapter Submission
October 1, 2021