Data Science with Semantic Technologies

This book PDF is perfect for those who love Computers genre, written by Archana Patel and published by CRC Press which was released on 20 June 2023 with total hardcover pages 293. You could read this book directly on your devices with pdf, epub and kindle format, check detail and related Data Science with Semantic Technologies books below.

Data Science with Semantic Technologies
Author : Archana Patel
File Size : 43,6 Mb
Publisher : CRC Press
Language : English
Release Date : 20 June 2023
ISBN : 9781000881233
Pages : 293 pages
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Data Science with Semantic Technologies by Archana Patel Book PDF Summary

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.

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

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Data Science with Semantic Technologies

Gone are the days when data was interlinked with related data by humans and human interpretation was required. Data is no longer just data. It is now considered a Thing or Entity or Concept with meaning, so that a machine not only understands the concept but also extrapolates the way

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Data Science with Semantic Technologies

DATA SCIENCE WITH SEMANTIC TECHNOLOGIES This book will serve as an important guide toward applications of data science with semantic technologies for the upcoming generation and thus becomes a unique resource for scholars, researchers, professionals, and practitioners in this field. To create intelligence in data science, it becomes necessary to

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Download or read online written by Anonim, published by Unknown which was released on . Get Books now! Available in PDF, ePub and Kindle.

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The Semantic Web for Knowledge and Data Management

Provides a single record of technologies and practices of the Semantic approach to the management, organization, interpretation, retrieval, and use of Web-based data.

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Semantic Data Mining

Ontologies are now increasingly used to integrate, and organize data and knowledge, particularly in data and knowledge-intensive applications in both research and industry. The book is devoted to semantic data mining – a data mining approach where domain ontologies are used as background knowledge, and where the new challenge is to

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Handbook of Research on Social Dimensions of Semantic Technologies and Web Services

"This book discusses the new technologies of semantic Web, transforming the way we use information and knowledge"--Provided by publisher.

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Data Quality Management with Semantic Technologies

Christian Fürber investigates the useful application of semantic technologies for the area of data quality management. Based on a literature analysis of typical data quality problems and typical activities of data quality management processes, he develops the Semantic Data Quality Management framework as the major contribution of this thesis.

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