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40343548
Semantic Modeling for Data: Avoiding Pitfalls and Breaking Dilemmas
90% of respondents would recommend this to a friend
Item #:
40343548
MYR 328
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This book shows you what semantic data modeling entails, and what challenges you have to face as a creator or user of semantic models.
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What Stands Out
Comprehensive Guide
This book provides an in-depth exploration of semantic modeling, equipping readers with essential techniques to navigate common pitfalls in data modeling effectively.
Real-World Applications
Offers practical examples and case studies that demonstrate how semantic modeling can solve complex data dilemmas faced by businesses today.
Expert Insights
Authored by knowledgeable experts, it delivers valuable insights and strategies, ensuring readers understand both foundational concepts and advanced techniques in semantic modeling.
Maklumat produk
- Learn the value of semantic data modeling and how to apply it in the real world
- Avoid pitfalls and overcome dilemmas in building high-quality semantic representations of data
- Understand fundamental concepts, phenomena, and processes related to semantic data modeling
- Discover effective frameworks and tools for semantic data modeling
- Avoid mistakes and bad practices that undermine data model creation
- Organize and execute semantic data initiatives, addressing technical, strategic, and organizational challenges
| Publisher | O'Reilly Media |
| Publication date | September 29, 2020 |
| Edition | 1st |
| Language | English |
| Print length | 328 pages |
| ISBN-10 | 1492054275 |
| ISBN-13 | 978-1492054276 |
| Item Weight | 2.31 pounds (1.05 kg) |
| Dimensions | 7 x 0.69 x 9.19 inches (17.8 x 1.8 x 23.3 cm) |
Who Should Buy?
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Data Analysts
Ideal for data analysts looking to enhance their semantic modeling skills and avoid common pitfalls in data interpretation.
-
Database Designers
Helpful for database designers who aim to implement effective semantic models to improve data organization and retrieval.
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Academics and Researchers
Beneficial for academics researching data semantics, providing insights into best practices and potential challenges in modeling.
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Beginners in Data
Not suitable for complete beginners in data who may struggle with complex concepts discussed in the book.
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Semantic Modeling for Data: Avoiding Pitfalls and Breaking Dilemmas
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Expert Systems Editorial Review
The Semantic Web book seems to be highly praised by customers working in ontology and taxonomy fields. They find the book to be clear and helpful in understanding the concepts of modeling, potential issues, and how to address them. Customers appreciate the book's ability to bridge the gap between web development perspectives for the semantic web and a more general approach to artificial intelligence. The book is described as an excellent and easy-to-read introduction that is recommended for those starting out or looking to refresh their knowledge of semantics. **** The Semantic Web book has received high praise from working professionals in the ontology and taxonomy fields, who find it to be a valuable resource for understanding and applying key concepts. The book offers great clarity in explaining the reasons behind modeling, addressing potential issues, and providing solutions. It effectively serves as a guide for beginners or those seeking to enhance their knowledge of semantics. With its unique approach in bridging the gap between web development and artificial intelligence perspectives, this book is a recommended read for anyone interested in these subjects. **
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- Clear and easy-to-understand explanations
- Bridges the gap between web development and artificial intelligence perspectives
- Valuable resource for ontology and taxonomy professionals
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MYR 328
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- Learn about semantic data modeling and its challenges
- Concrete advice on avoiding dangers and overcoming obstacles
- Teaches fundamental and enduring semantic modeling principles
- Improve the quality and usability of the models you build
- For data practitioners and those working with semantic representations of data
- Ideal for taxonomists, ontologists, and data modelers
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