Data Quality Profiling at Scale with Progressive Sampling: A Benchmark for Data-Centric AI Pipelines
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Computer Science > Databases
arXiv:2607.25356 (cs)
[Submitted on 28 Jul 2026]
Title:Data Quality Profiling at Scale with Progressive Sampling: A Benchmark for Data-Centric AI Pipelines
Authors:Laure Berti-Equille
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Abstract: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.
| Comments: | 29 pages, 8 figures |
| Subjects: | Databases (cs.DB); Artificial Intelligence (cs.AI); Computation and Language (cs.CL) |
| ACM classes: | H.2.8; G.3; H.2.4; I.2.6 |
| Cite as: | arXiv:2607.25356 [cs.DB] |
| (or arXiv:2607.25356v1 [cs.DB] for this version) | |
| https://doi.org/10.48550/arXiv.2607.25356
arXiv-issued DOI via DataCite (pending registration)
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| Journal reference: | Transactions on Large-Scale Data and Knowledge-Centered Systems Special Issue on Advances in Data-Centric Artificial Intelligence (2026) |
Submission history
From: Laure Berti-Equille [view email][v1] Tue, 28 Jul 2026 07:03:50 UTC (171 KB)
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