Status : Verified
| Personal Name | Soytong, Maywadee |
|---|---|
| Resource Title | Data Architecture Design for Integrated Philippine Road Safety Datasets |
| Date Issued | 25 February 2026 |
| Abstract | Road safety incident data remain challenging to manage and analyze, particularly in large urban areas such as Metro Manila. This dissertation presents a data model and storage architecture to support road safety analysis and inter-agency data integration. The study analyzes existing road safety incident datasets and international standards to examine data fields, entity types, and their relationships. Based on this analysis, a unified data model is proposed using the Data Vault 2.0 methodology. The proposed model supports complex incident scenarios, explicit semantic relationships and incremental data integration from multiple agencies. To implement and evaluate the model, this study develops a data lakehouse architecture based on opensource technologies. Apache Iceberg and Nessie are used within the Dremio platform to manage structured data and support batch and near-real-time workflows. Apache Spark and Jupyter Notebook are used for data ingestion and processing, while Apache Superset supports data exploration and visualization. The proposed framework addresses key challenges in public-sector data systems, including data fragmentation, limited interoperability, and slow analytics. It also provides a foundation for future extensions, such as advanced analytics and automated event detection, to further support road safety planning and decision-making. |
| Degree Course | Doctor of Philosophy in Computer Science |
| Language | English |
| Keyword | Data Architecture; Data integration; Philippine; Road Safety |
| Material Type | Thesis/Dissertation |
Preliminary Pages
2.35 Mb
Category : F - Regular work, i.e., it has no patentable invention or creation, the author does not wish for personal publication, there is no confidential information.
Access Permission : Open Access