Research Data Engineering

Approximation and Hardness for the Data Validation Problem

Nov 4, 2026 Source ↗

Key Insights

Finnah, Gönsch and Otto [Eur.
J.
Oper.

Cite this synthesis DOI: 10.48550/arXiv.2611.06773 ↗

Show formatted citation

@misc{ acaciadata-research-approximation-and-hardness-for-the-data-validation-problem,
  title = { Approximation and Hardness for the Data Validation Problem },
  author = { Leszek },
  year = { 2026 },
  doi = { 10.48550/arXiv.2611.06773 },
  url = { http://arxiv.org/abs/2611.06773v1 },
  note = {Summarized and classified by AcaciaFund}
}

TY  - GEN
TI  - Approximation and Hardness for the Data Validation Problem
AU  - Leszek
PY  - 2026
DO  - 10.48550/arXiv.2611.06773
UR  - http://arxiv.org/abs/2611.06773v1
ER  -

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Overview

Finnah, Gönsch and Otto [Eur. J. Oper.

Res. 2025] recently introduced the Data Validation Problem (DVP): Given a transitive directed acyclic graph $G$ on $n$ vertices (resp. tasks) and $m$ edges (resp

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Data Quality: A theme covered by this item within the data engineering context.

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Understand how Approximation and Hardness for the Data Validation Problem relates to data engineering workflows and controls.
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Article Metadata

Evergreen Synthesized

dataops data-quality

Data Quality DataOps

SQI 0.750