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Recommendations for Evolving Relational Databases

Abstract : Relational databases play a central role in many information systems. Their schemas contain structural and behavioral entity descriptions. Databases must continuously be adapted to new requirements of a world in constant change while: (1) relational database management systems (RDBMS) do not allow inconsistencies in the schema; (2) stored procedure bodies are not meta-described in RDBMS such as PostgreSQL that consider their bodies as plain text. As a consequence , evaluating the impact of an evolution of the database schema is cumbersome , being essentially manual. We present a semi-automatic approach based on recommendations that can be compiled into a SQL patch fulfilling RDBMS constraints. To support recommendations, we designed a meta-model for relational databases easing computation of change impact. We performed an experiment to validate the approach by reproducing a real evolution on a database. The results of our experiment show that our approach can set the database in the same state as the one produced by the manual evolution in 75% less time.
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https://hal.inria.fr/hal-02511466
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Submitted on : Wednesday, March 18, 2020 - 6:20:17 PM
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Julien Delplanque, Anne Etien, Nicolas Anquetil, Stéphane Ducasse. Recommendations for Evolving Relational Databases. CAiSE 2020 - 32nd International Conference on Advanced Information Systems Engineering, Jun 2020, Grenoble, France. ⟨hal-02511466⟩

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