Handbook Of Data Science With Semantic Technologies
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Handbook of Data Science with Semantic Technologies
As the world has entered the era of big data, there is a need to give a semantic perspective to the data to find unseen patterns, derive meaningful information, and make intelligent decisions. This 2-volume handbook set is a unique, comprehensive, and complete presentation of the current progress and future potential explorations in the field of data science and related topics. Handbook of Data Science with Semantic Technologies provides a roadmap for a new trend and future development of data science with semantic technologies. The first volume serves as an important guide towards applications of data science with semantic technologies for the upcoming generation and thus becomes a unique resource for both academic researchers and industry professionals. The second volume provides a roadmap for the deployment of semantic technologies in the field of data science that enables users to create intelligence through these technologies by exploring the opportunities while eradicating the current and future challenges. The set explores the optimal use of these technologies to provide the maximum benefit to the user under one comprehensive source. This set consisting of two separate volumes can be utilized independently or together as an invaluable resource for students, scholars, researchers, professionals, and practitioners in the field.
Data Science with Semantic Technologies
As data is an important asset for any organization, it is essential to apply semantic technologies in data science to fulfill the need of any organization. This first volume of a two-volume handbook set provides a roadmap for new trends and future developments of data science with semantic technologies. Data Science with Semantic Technologies: New Trends and Future Developments highlights how data science enables the user to create intelligence through these technologies. In addition, this book offers the answers to various questions such as: Can semantic technologies facilitate data science? Which type of data science problems can be tackled by semantic technologies? How can data scientists benefit from these technologies? What is the role of semantic technologies in data science? What is the current progress and future of data science with semantic technologies? Which types of problems require the immediate attention of the researchers? What should be the vision 2030 for data science? This volume can serve as an important guide toward applications of data science with semantic technologies for the upcoming generation and, thus, it is a unique resource for scholars, researchers, professionals, and practitioners in this field.
Handbook of Data Science Approaches for Biomedical Engineering
Author: Valentina Emilia Balas
language: en
Publisher: Academic Press
Release Date: 2019-11-13
Handbook of Data Science Approaches for Biomedical Engineering covers the research issues and concepts of biomedical engineering progress and the ways they are aligning with the latest technologies in IoT and big data. In addition, the book includes various real-time/offline medical applications that directly or indirectly rely on medical and information technology. Case studies in the field of medical science, i.e., biomedical engineering, computer science, information security, and interdisciplinary tools, along with modern tools and the technologies used are also included to enhance understanding. Today, the role of Big Data and IoT proves that ninety percent of data currently available has been generated in the last couple of years, with rapid increases happening every day. The reason for this growth is increasing in communication through electronic devices, sensors, web logs, global positioning system (GPS) data, mobile data, IoT, etc. - Provides in-depth information about Biomedical Engineering with Big Data and Internet of Things - Includes technical approaches for solving real-time healthcare problems and practical solutions through case studies in Big Data and Internet of Things - Discusses big data applications for healthcare management, such as predictive analytics and forecasting, big data integration for medical data, algorithms and techniques to speed up the analysis of big medical data, and more