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.
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