A Model-Driven Pipeline for Data Quality Specification and Operationalization: A No-Code Approach for Domain Experts
2026-07-27 • Information Retrieval
Information Retrieval
AI summaryⓘ
The authors address the problem of keeping data accurate in fields like cultural heritage, where data is often entered by hand and can have errors. Experts know what good data should look like but usually can't write the technical rules to check the data automatically. To fix this, the authors created a system that helps turn expert knowledge into reusable, automated checks to find data problems more easily. Their solution includes a tool called Constrainify that lets experts customize quality rules and turn them into computer programs without needing lots of technical skill.
data qualitycultural heritagequality analysisdomain expertsdata validationmetamodelmodel-driven engineeringConstrainifyautomated data checksreusable templates
Authors
Arno Kesper, Lukas Sebastian Hofmann, Markus Matoni, Gabriele Taentzer
Abstract
High-quality data is essential for reliable analysis, decision-making, and research across domains. This is especially relevant in areas such as cultural heritage, where data is collected and curated manually, making it prone to quality issues like inconsistencies. To improve data quality, the data must be analyzed regularly using systematic quality analyses. Quality analyses validate the conformance of data to domain-specific expectations. These expectations are best understood by domain experts, who can express them using natural language. However, they rarely possess the technical expertise to formalize these expectations into executable quality analyses. Consequently, this process requires domain experts and data engineers, making it time-consuming and technically demanding. The required technical expertise and the resulting dependencies pose a significant challenge. To address this challenge, we present a pipeline for formalizing and operationalizing data quality constraints. We support this pipeline using QPM, a metamodel for defining templates for reusable quality analyses. The web application Constrainify enables tailoring templates to specific conceptual requirements and translating them into executable quality analyses via a tool-chain based on model-driven engineering subpipelines. The result is a set of reusable, repeatable, and domain-specific quality analyses.