Enhancing data models with tuning transformations
| dc.contributor.author | Mattinson, Jason, E. | |
| dc.contributor.author | McAllister, Andrew, J. | |
| dc.date.accessioned | 2023-03-01T18:30:06Z | |
| dc.date.available | 2023-03-01T18:30:06Z | |
| dc.description.abstract | Maintaining consistency between an entity-relationship model and the relational database it represents can be problematic. As a database evolves, reverse engineering can be used periodically to capture an up-to-date model. There are difficulties, however, in obtaining accurate conceptual data requirements using automated reverse engineering tools. A new approach is proposed that ensures data models are up to date and removes the need for reverse engineering. Tuning transformations are introduced as a concise method for specifying database design modifications related to performance tuning. This method reduces the effort required to keep models up to date. Maintaining an entity-relationship model together with concise specifications of tuning transformations enables a tuned relational database definition to be generated automatically. Keywords: conceptual data model, relational database, reverse engineering, physical database design tuning transformations | |
| dc.description.copyright | Copyright @ Jason E. Mattinson and Andrew J. McAllister. | |
| dc.identifier.uri | https://unbscholar.lib.unb.ca/handle/1882/14972 | |
| dc.rights | http://purl.org/coar/access_right/c_abf2 | |
| dc.subject.discipline | Computer Science | |
| dc.title | Enhancing data models with tuning transformations | |
| dc.type | technical report |
Files
Original bundle
1 - 1 of 1