The Missing Pieces in Classical and Modern Data Error Classification - Nascimento, Dimas Cassimiro; Silva, Daliton da (Anais do Simpósio Brasileiro de Banco de Dados (SBBD); 2026: Anais do XLI Simpósio Brasileiro de Bancos de DadosDO - 10.5753/sbbd.2026.249431, 2026)
Data quality research has produced extensive catalogs of data error types and data quality problems. However, gaps remain when contrasting classical taxonomies with recent consolidated surveys. This paper identifies three categories of data errors that are insufficiently captured in both foundational and contemporary works: ambiguous abbreviation values, intra-tuple dependency violations based on reference semantics, and cross-source unit inconsistencies. We provide formal definitions for each error type, illustrate them with representative examples, and discuss practical considerations for their detection in real-world scenarios. Our contributions help bridge classical data quality literature with modern data integration challenges, supporting a more comprehensive assessment of data quality in heterogeneous environments.