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.
