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edbt-experiments

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Use when designing or auditing EDBT empirical evaluations for database-systems work, covering real workloads and datasets, fair and tuned baselines, honest measurement across realistic scales, reproducible harnesses, and the higher bar of the Experiments & Analysis paper where the measurement itself is the contribution.

EDBT-Skills/skills/edbt-experiments/SKILL.md

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# EDBT Experiments

Use this before submission when the evaluation is not yet locked. EDBT reviewers are database-systems
empiricists; the evaluation is where a good idea is won or lost. The organizing principle is
**evidence proportional to the claim** — the study must measure the thing the paper actually asserts,
on workloads, datasets, and scales a skeptic would accept, against baselines a skeptic would accept.

## Evaluation audit

- **Match evidence to the claim shape.** A claim about *latency* needs latency measurements under a
  realistic workload; a claim about *scalability* needs runs across a realistic range of sizes/nodes;
  a claim about *space* needs memory/footprint numbers; a claim about *accuracy* needs a labeled
  ground truth. "Faster on a dataset" is not evidence for a scalability claim.
- **Use real workloads and datasets,** named and sourced (standard benchmarks, real query logs, real
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