docs: add relatorio de analise de generalizacao para repositorios abertos#7
docs: add relatorio de analise de generalizacao para repositorios abertos#7profsergiocosta wants to merge 3 commits intomainfrom
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…rtos This commit introduces a detailed architectural analysis report (`relatorio_analise_generalizacao.md`) assessing the potential of generalizing the current DBAcademic ETL pipeline. The report evaluates: - The current Apache Airflow, Pandas, and Simpot-based architecture. - Strengths and bottlenecks in extracting data from open repositories (e.g., CKAN APIs vs. Generic REST APIs). - Limitations of hardcoded semantic mapping (`models.py`) for converting arbitrary data to Linked Data/RDF. - Architectural recommendations including the adoption of RML/YARRRML, SHACL/ShEx, and a generic dynamic discovery pipeline. Co-authored-by: profsergiocosta <86836+profsergiocosta@users.noreply.github.com>
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This commit refactors the generalization analysis report (`relatorio_analise_generalizacao.md`) to strongly recommend migrating the Linked Data conversion tool from `simpot` to `rdfmapper`. Key updates include: - Explicitly identifying the limitations of `simpot` (lack of dynamic querying, missing SHACL validation, intrusive domain rules). - Adding a dedicated section (5.1) detailing how `rdfmapper` solves these architectural gaps. - Highlighting `rdfmapper` features such as automatic SHACL shape generation, dynamic `RDFRepository` queries, and streamlined relationships (One-to-One and One-to-Many). - Introducing a meta-programming approach in "Mapeamento Semântico Desacoplado" to leverage `rdfmapper` dynamically based on JSON configuration instead of hardcoded Python classes. Co-authored-by: profsergiocosta <86836+profsergiocosta@users.noreply.github.com>
This commit extracts actionable implementation issues from the previously created generalization analysis report and saves them in `issues_priorizadas.md`. The issues are prioritized into: - High Priority (🔴): Refactoring `models.py` from `simpot` to `rdfmapper` (including SHACL validation and decorators) and decoupling semantic mappings to `config.py`. - Medium Priority (🟡): Building a generic `GenericRestConsumer` to support pagination and configurable headers for open data portals. - Low Priority (🟢): Implementing a discovery pipeline (`discovery_dag.py`) for automated CKAN dataset listing. Co-authored-by: profsergiocosta <86836+profsergiocosta@users.noreply.github.com>
Adiciona o relatório de análise sobre o potencial do repositório ser generalizado para extrair dados de quaisquer repositórios abertos (portais de transparência, CKAN, etc) e converter para dados conectados (Linked Data / RDF).
O relatório aborda:
models.pyPR created automatically by Jules for task 13602552557790377385 started by @profsergiocosta