With the growing maturity and stability of digitization and edge
technologies, vast numbers of digital entities, connected devices, and
microservices interact purposefully to create huge sets of
poly-structured digital data. Corporations are continuously seeking
fresh ways to use their data to drive business innovations and
disruptions to bring in real digital transformation. Data science (DS)
is proving to be the one-stop solution for simplifying the process of
knowledge discovery and dissemination out of massive amounts of
multi-structured data.
Supported by query languages, databases, algorithms, platforms,
analytics methods and machine and deep learning (ML and DL) algorithms,
graphs are now emerging as a new data structure for optimally
representing a variety of data and their intimate relationships.
Compared to traditional analytics methods, the connectedness of data
points in graph analytics facilitates the identification of clusters of
related data points based on levels of influence, association,
interaction frequency and probability. Graph analytics is being
empowered through a host of path-breaking analytics techniques to
explore and pinpoint beneficial relationships between different entities
such as organizations, people and transactions. This edited book aims to
explain the various aspects and importance of graph data science. The
authors from both academia and industry cover algorithms, analytics
methods, platforms and databases that are intrinsically capable of
creating business value by intelligently leveraging connected data.
This book will be a valuable reference for ICTs industry and academic
researchers, scientists and engineers, and lecturers and advanced
students in the fields of data analytics, data science, cloud/fog/edge
architecture, internet of things, artificial intelligence/machine and
deep learning, and related fields of applications. It will also be of
interest to analytics professionals in industry and IT operations teams.
