Managing semantic evolution in databases: from theory to implementation
Resumo
Long-lived databases undergo continuous semantic change: categories are renamed, standards revised, boundaries merged or split. Unlike schema evolution, these value-level shifts lack principled solutions, leaving users to reconcile discrepancies by hand. This thesis formalizes semantic evolution as time-stamped operations over an interval-based storage model, and derives two approaches that answer queries transparently: query rewriting and data preprocessing. Implemented in MellowDB, a MongoDB-based Python middleware, both proved production-ready on 30 years of Brazilian mortality records: preprocessing excels in read-oriented scenarios, whereas query rewriting becomes preferable as writes dominate.
Palavras-chave:
semantic evolution, database, query rewriting, query preprocessing
Referências
Hakimpour, F. and Geppert, A. (2005). Resolution of semantic heterogeneity in database schema integration using formal ontologies. Information Technology and Management, 6:97–122.
Herrmann, K., Voigt, H., Behrend, A., Rausch, J., and Lehner, W. (2017). Living in parallel realities: Co-existing schema versions with a bidirectional database evolution language. In Proceedings of the 2017 ACM International Conference on Management of Data, SIGMOD/PODS, pages 1101–1116.
Klettke, M., Storl, U., Shenavai, M., and Scherzinger, S. (2016). NoSQL schema evolution and big data migration at scale. In 2016 IEEE International Conference on Big Data, Big Data, pages 2764–2774.
Mergen, S. L. S. and Heuser, C. A. (2006). Data translation between taxonomies. In International Conference on Advanced Information Systems Engineering, pages 111–124. Springer.
Moller, M. L., Klettke, M., Hillenbrand, A., and Störl, U. (2019). Query rewriting for continuously evolving NoSQL databases. In International Conference on Conceptual Modeling, ER, pages 213–221.
Moon, H. J., Curino, C. A., Deutsch, A., Hou, C.-Y., and Zaniolo, C. (2008). Managing and querying transaction-time databases under schema evolution. Proceedings of the VLDB Endowment, 1(1):882–895.
Ventrone, V. (1991). Semantic heterogeneity as a result of domain evolution. ACM SIGMOD Record, 20(4):16–20.
Herrmann, K., Voigt, H., Behrend, A., Rausch, J., and Lehner, W. (2017). Living in parallel realities: Co-existing schema versions with a bidirectional database evolution language. In Proceedings of the 2017 ACM International Conference on Management of Data, SIGMOD/PODS, pages 1101–1116.
Klettke, M., Storl, U., Shenavai, M., and Scherzinger, S. (2016). NoSQL schema evolution and big data migration at scale. In 2016 IEEE International Conference on Big Data, Big Data, pages 2764–2774.
Mergen, S. L. S. and Heuser, C. A. (2006). Data translation between taxonomies. In International Conference on Advanced Information Systems Engineering, pages 111–124. Springer.
Moller, M. L., Klettke, M., Hillenbrand, A., and Störl, U. (2019). Query rewriting for continuously evolving NoSQL databases. In International Conference on Conceptual Modeling, ER, pages 213–221.
Moon, H. J., Curino, C. A., Deutsch, A., Hou, C.-Y., and Zaniolo, C. (2008). Managing and querying transaction-time databases under schema evolution. Proceedings of the VLDB Endowment, 1(1):882–895.
Ventrone, V. (1991). Semantic heterogeneity as a result of domain evolution. ACM SIGMOD Record, 20(4):16–20.
Publicado
08/09/2026
Como Citar
NEPOMUCENO, Pedro Ivo Siqueira; BRAGHETTO, Kelly Rosa.
Managing semantic evolution in databases: from theory to implementation. In: CONCURSO DE TESES E DISSERTAÇÕES (CTDBD) - SIMPÓSIO BRASILEIRO DE BANCO DE DADOS (SBBD), 41. , 2026, São Carlos/SP.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2026
.
p. 355-359.
DOI: https://doi.org/10.5753/sbbd_estendido.2026.249408.
