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This practical guidebook describes the basic concepts, the
mathematical developments, and the engineering methodologies for
exploiting possibility theory for the computer-based design of an
information fusion system where the goal is decision support for
industries in smart ICT (information and communications
technologies). This exploitation of possibility theory improves
upon probability theory, complements Dempster-Shafer theory, and
fills an important gap in this era of Big Data and Internet of
Things. The book discusses fundamental possibilistic concepts:
distribution, necessity measure, possibility measure, joint
distribution, conditioning, distances, similarity measures,
possibilistic decisions, fuzzy sets, fuzzy measures and integrals,
and finally, the interrelated theories of uncertainty..uncertainty.
These topics form an essential tour of the mathematical tools
needed for the latter chapters of the book. These chapters present
applications related to decision-making and pattern recognition
schemes, and finally, a concluding chapter on the use of
possibility theory in the overall challenging design of an
information fusion system. This book will appeal to researchers and
professionals in the field of information fusion and analytics,
information and knowledge processing, smart ICT, and decision
support systems.
This book presents a contemporary view of the role of information
quality in information fusion and decision making, and provides a
formal foundation and the implementation strategies required for
dealing with insufficient information quality in building fusion
systems for decision making. Information fusion is the process of
gathering, processing, and combining large amounts of information
from multiple and diverse sources, including physical sensors to
human intelligence reports and social media. That data and
information may be unreliable, of low fidelity, insufficient
resolution, contradictory, fake and/or redundant. Sources may
provide unverified reports obtained from other sources resulting in
correlations and biases. The success of the fusion processing
depends on how well knowledge produced by the processing chain
represents reality, which in turn depends on how adequate data are,
how good and adequate are the models used, and how accurate,
appropriate or applicable prior and contextual knowledge is. By
offering contributions by leading experts, this book provides an
unparalleled understanding of the problem of information quality in
information fusion and decision-making for researchers and
professionals in the field.
This book focuses on one of the major challenges of the newly
created scientific domain known as data science: turning data into
actionable knowledge in order to exploit increasing data volumes
and deal with their inherent complexity. Actionable knowledge has
been qualitatively and intensively studied in management, business,
and the social sciences but in computer science and engineering,
its connection has only recently been established to data mining
and its evolution, 'Knowledge Discovery and Data Mining' (KDD).
Data mining seeks to extract interesting patterns from data, but,
until now, the patterns discovered from data have not always been
'actionable' for decision-makers in Socio-Technical Organizations
(STO). With the evolution of the Internet and connectivity, STOs
have evolved into Cyber-Physical and Social Systems (CPSS) that are
known to describe our world today. In such complex and dynamic
environments, the conventional KDD process is insufficient, and
additional processes are required to transform complex data into
actionable knowledge. Readers are presented with advanced knowledge
concepts and the analytics and information fusion (AIF) processes
aimed at delivering actionable knowledge. The authors provide an
understanding of the concept of 'relation' and its exploitation,
relational calculus, as well as the formalization of specific
dimensions of knowledge that achieve a semantic growth along the
AIF processes. This book serves as an important technical
presentation of relational calculus and its application to
processing chains in order to generate actionable knowledge. It is
ideal for graduate students, researchers, or industry professionals
interested in decision science and knowledge engineering.
This book focuses on one of the major challenges of the newly
created scientific domain known as data science: turning data into
actionable knowledge in order to exploit increasing data volumes
and deal with their inherent complexity. Actionable knowledge has
been qualitatively and intensively studied in management, business,
and the social sciences but in computer science and engineering,
its connection has only recently been established to data mining
and its evolution, 'Knowledge Discovery and Data Mining' (KDD).
Data mining seeks to extract interesting patterns from data, but,
until now, the patterns discovered from data have not always been
'actionable' for decision-makers in Socio-Technical Organizations
(STO). With the evolution of the Internet and connectivity, STOs
have evolved into Cyber-Physical and Social Systems (CPSS) that are
known to describe our world today. In such complex and dynamic
environments, the conventional KDD process is insufficient, and
additional processes are required to transform complex data into
actionable knowledge. Readers are presented with advanced knowledge
concepts and the analytics and information fusion (AIF) processes
aimed at delivering actionable knowledge. The authors provide an
understanding of the concept of 'relation' and its exploitation,
relational calculus, as well as the formalization of specific
dimensions of knowledge that achieve a semantic growth along the
AIF processes. This book serves as an important technical
presentation of relational calculus and its application to
processing chains in order to generate actionable knowledge. It is
ideal for graduate students, researchers, or industry professionals
interested in decision science and knowledge engineering.
This practical guidebook describes the basic concepts, the
mathematical developments, and the engineering methodologies for
exploiting possibility theory for the computer-based design of an
information fusion system where the goal is decision support for
industries in smart ICT (information and communications
technologies). This exploitation of possibility theory improves
upon probability theory, complements Dempster-Shafer theory, and
fills an important gap in this era of Big Data and Internet of
Things. The book discusses fundamental possibilistic concepts:
distribution, necessity measure, possibility measure, joint
distribution, conditioning, distances, similarity measures,
possibilistic decisions, fuzzy sets, fuzzy measures and integrals,
and finally, the interrelated theories of uncertainty..uncertainty.
These topics form an essential tour of the mathematical tools
needed for the latter chapters of the book. These chapters present
applications related to decision-making and pattern recognition
schemes, and finally, a concluding chapter on the use of
possibility theory in the overall challenging design of an
information fusion system. This book will appeal to researchers and
professionals in the field of information fusion and analytics,
information and knowledge processing, smart ICT, and decision
support systems.
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