- ホーム
- > 洋書
- > 英文書
- > Computer / General
Full Description
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.
Contents
Chapter 1: Toward graph data science
Chapter 2: Data science: the Artificial Intelligence (AI) algorithms-inspired use cases
Chapter 3: Accelerating graph analytics
Chapter 4: Introduction to IoT data analytics and its use cases
Chapter 5: Demystifying digital transformation technologies in healthcare
Chapter 6: Semantic knowledge graph technologies in data science
Chapter 7: Why graph analytics?
Chapter 8: Graph technology: a detailed study of trending techniques and technologies of graph analytics
Chapter 9: A holistic analysis to identify the efficiency of data growth using a standardized method of non-functional requirements in graph applications
Chapter 10: Roadmap of integrated data analytics - practices, business strategies and approaches
Chapter 11: Introduction to graph analytics
Chapter 12: A study of graph analytics for massive datasets
Chapter 13: Demystifying graph AI
Chapter 14: Application of graph data science and graph databases in major industries
Chapter 15: Graph data science for cybersecurity
Chapter 16: The machine learning algorithms for data science applications
