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vldb-experiments
Use when designing or auditing the evaluation of a VLDB paper, covering workload and dataset realism at scale, competitor tuning fairness, scalability curves versus single points, tail-latency and throughput reporting, ablations that isolate the mechanism, and the loss-case disclosure PVLDB reviewers look for first.
VLDB-Skills/skills/vldb-experiments/SKILL.md
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# VLDB Experiments
Use this before the evaluation section hardens. At this venue the experiments
*are* the argument: a PVLDB reviewer typically flips from the introduction
straight to the plots and decides how skeptically to read everything else.
## What the evaluation must establish
Four distinct burdens, each needing its own experiments:
1. **The problem exists** — measure the baseline failing on a credible
workload before showing your fix.
2. **The mechanism causes the gain** — ablate your components; a monolithic
"our system vs. theirs" plot proves selection, not mechanism.