大规模数据质量检测中，随机采样比依赖领域知识的采样更准更快。
Data Quality Profiling at Scale with Progressive Sampling: A Benchmark for Data-Centric AI Pipelines
- 用九种采样法对比，随机均匀采样在多数场景表现最优。
- 5%采样预算下，随机采样平均相对误差仅0.49%，远优于其他方法。
- 关键发现：数据分布代表性比专家先验更重要，无需复杂策略。
数据质量分析（如缺失率、重复率、异常值密度、函数依赖违规）是数据驱动AI流程的基础，但对数百万行数据进行全量扫描耗时过长，难以支持近实时监控。现有方案普遍采用渐进式采样，但哪种策略在规模下能最好保持分析精度仍不清楚。本文在三个真实数据集（NYC 311、NYPD逮捕记录、UCI Adult；最多50万行）、一个物联网传感器流（230万行）、两个超大规模真实数据集（包括超马拉松跑步数据，最多740万行）及扩展至500万行的合成数据上，系统评测了九种采样策略：盲采（随机均匀、几何、Yamane、聚类）与代理引导型（马尔可夫链蒙特卡洛、有向无环图DAG、按列类型或质量评分分层、重要性加权）。结果表明，盲采中的随机均匀采样在所有真实数据集上表现最佳：5%采样预算下，纽约311数据平均相对误差仅为0.49%；而基于DAG的MCMC方法误差高达19.5%（约40倍更差），整体表现差11–49倍（Wilcoxon检验，p=0.002，n=9对）。聚类采样与随机均匀相当（均值相对误差分别为0.110与0.111），而代理引导方法均出现类似失败模式（0.20–0.35）。在超大规模数据下，随机均匀采样呈近线性增长（O(N^0.964)），而DAG方法为超线性（O(N^1.272)），速度慢28–47倍且精度差6倍。根本原因在于箱线图（IQR）代理不匹配：代理引导方法过度关注数值异常值，而实际质量问题集中于其无法捕捉的分类字段。结论：采样质量取决于数据分布的代表性，而非领域知识；生产级质量分析中，无需复杂策略，仅需无模式随机均匀或聚类采样即可。
原文摘要 · Abstract (English)
Data quality profiling -- computing missing-value rates, duplicate fractions, outlier densities, and functional-dependency violations -- is foundational for data-centric AI pipelines, yet exhaustive scans over millions of rows are prohibitively slow for near-real-time monitoring. Progressive sampling is the standard alternative; the open question is which strategy best preserves profile fidelity at scale. We benchmark nine sampling strategies -- blind (random uniform, geometric, Yamane, cluster) and proxy-guided (Metropolis-Hastings, DAG, stratified by column type or quality score, importance-weighted) -- on three real-world datasets (NYC 311, NYPD arrests, UCI Adult; up to 500K rows), an IoT sensor stream (2.3M rows), two ultra-large real datasets including Ultra-Marathon Running (up to 7.4M rows), and synthetic data scaled to 5x10^6 rows. Contrary to the assumption sharpens estimates, blind representative samplers dominate uniformly. At a 5% budget, random uniform achieves 0.49% mean relative error on NYC 311; DAG-guided MCMC yields 19.5% (approx. 40x worse), and across all real datasets DAG is 11-49x worse (Wilcoxon W=0, p=0.002, n=9 pairs). Cluster sampling matches random uniform (MRE 0.110 vs. 0.111); proxy-guided methods share DAG's failure mode (MRE 0.20-0.35). At scale, random uniform is near-linear (O(N^{0.964})) while DAG is super-linear (O(N^{1.272})), running 28--47x slower on ultra-large data with 6x worse accuracy. The root cause is an IQR proxy mismatch: proxy-guided samplers over-pursue numeric outliers, while quality defects concentrate in categorical columns invisible to the proxy. The actionable finding: representativeness, not domain knowledge, determines sampler quality -- schema-free random uniform or cluster sampling suffices for production-grade quality profiling at scale.
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