Generating SQL Queries from Spoken Natural Language
| dc.contributor.author | Seghir Birem, Othman | |
| dc.contributor.author | BRAHIMI, Mahmoud: Supervisor | |
| dc.date.accessioned | 2024-07-15T10:13:12Z | |
| dc.date.available | 2024-07-15T10:13:12Z | |
| dc.date.issued | 2024-06 | |
| dc.description.abstract | In today's digital era, organizations grapple with the challenge of managing vast datasets, necessitating sophisticated tools for data analysis. While Structured Query Language (SQL) serves as a robust choice for database interaction, its complexity poses barriers, especially for nontechnical users. This project introduces SQUIRE, a model designed to bridge this gap by offering an intuitive Natural Language Database Interface (NLDBI) for SQL relational databases. SQUIRE aims to democratize data access, empowering users to generate diverse SQL queries without extensive SQL knowledge, thereby enhancing flexibility in data retrieval. | |
| dc.identifier.uri | https://dspace.univ-msila.dz/handle/123456789/43777 | |
| dc.language.iso | en | |
| dc.publisher | University of Mohamed Boudiaf, M’sila | |
| dc.subject | SQL | |
| dc.subject | Data Base | |
| dc.subject | SQUIRE | |
| dc.subject | NLDBI | |
| dc.subject | Python | |
| dc.title | Generating SQL Queries from Spoken Natural Language | |
| dc.type | Thesis |