Rampart: Reproducible Benchmarking of Data Processing Paradigms with Automated Anti-Leakage Verification - Xavier, Eos; Ripardo, Lívia R. Duarte; Mota, Fernando M. da; Nogueira, Bruno M.; Borges, Vanessa A. (Anais do Simpósio Brasileiro de Banco de Dados (SBBD); 2026: Anais do XLI Simpósio Brasileiro de Bancos de Dados, 2026)
The growing adoption of data-processing engines for machine-learning pipelines has increased the need for rigorous benchmarks that ensure validity and reproducibility. However, many studies still lack mechanisms to prevent data leakage. Rampart is presented as an open-source framework executing the same walk-forward validation pipeline across three data-processing paradigms while enforcing anti-leakage invariants. Rampart treats prediction equivalence as a validity clause for cross-paradigm latency comparisons and produces bitwise-reproducible executions. Evaluated on two public time-series panels, the framework reveals a scale-dependent latency crossover and supports inclusion of new paradigms with low engineering overhead.