Semantic modeling for data avoiding pitfalls and breaking dilemmas
"What value does semantic data modeling offer? As an information architect or data science professional, let's say you have an abundance of the right data and the technology to extract business gold-- but you still fail. The reason? Bad data semantics. In this practical and comprehensive f...
| Otros Autores: | |
|---|---|
| Formato: | Libro electrónico |
| Idioma: | Inglés |
| Publicado: |
Sebastopol, CA :
O'Reilly Media
2020.
|
| Edición: | First edition |
| Materias: | |
| Ver en Biblioteca Universitat Ramon Llull: | https://discovery.url.edu/permalink/34CSUC_URL/1im36ta/alma991009631134506719 |
Tabla de Contenidos:
- Part 1. The basics. Mind the semantic gap
- Semantic modeling elements
- Semantic and linguistic phenomena
- Semantic model quality
- Semantic model development
- Part 2. The pitfalls. Bad descriptions
- Bad semantics
- Bad model specification and knowledge acquisition
- Bad quality management
- Bad application
- Bad strategy and organization
- Part 3. The dilemmas. Representation dilemmas
- Expressiveness and content dilemmas
- Evolution and governance dilemmas
- Looking ahead.
