Data Science with Semantic Technologies: New Trends and Future Developments

ISBN-13: 9781032316666 / Angielski / Twarda / 2023 / 336 str.

Archana Patel; Narayan C. Debnath

Opis

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.

Spis treści

1. What is Data Science.  2. Big Data and its Future.  3. Smart Warehouse Testbed: From Conceptual Framework to a Real Project.  4. Empirical Study on Sentiment Analysis.  5. Forecasting on Covid-19 Data Using ARIMAX Model.  6. ML-Based Method for Detecting and Alerting to Cyber Attacks.  7. Machine Learning in Natural Language Processing-Emerging Trends and Challenges.  8. Machine Learning and Future Directions.  9. Towards a Web Standard for Neurosymbolic Integration and Knowledge Representation Using Model Cards.  10. Semantic Web Technologies.  11. Data Science with Semantic Technologies.  12. Ontological Perspective in Cancer Care System.  13. Interoperability Frameworks: Data Fabric and Data Mesh Architectures.  14. Recommender System for E-commerce: How Ontologies Support Recommendations.