15th International Workshop on Quality in Databases (QDB’26)

Data quality has been a major concern of organizations for decades, leading to the introduction of standards and quality frameworks. Recent advances in artificial intelligence (AI), e.g., generative AI, have brought data quality (DQ) back into the spotlight. In enterprises, it is particularly important to build data ecosystems that can cope with the emerging challenges posed by AI-based systems. Data quality has been tackled from different perspectives: the database community has made significant advances in data profiling and data cleaning and still focuses on DQ issues like duplicate detection or missing data handling; the information systems community provides solutions for addressing DQ at an organizational level; the machine learning (ML) community focuses mainly on the development of robust models that can deal with issues in the data. We want to build upon the success of QDB 23, QDB 24, and QDB 25 and continue offering an open format for joint discussions between different communities on the future of DQ assessment and improvement.

QDB 2025 Proceedings: https://www.vldb.org/2025/Workshops/vldb.html#outline-container-org2a44ac9

QDB 2024 Proceedings: https://vldb.org/workshops/2024/#outline-container-org90551d8

QDB 2023 Proceedings: https://ceur-ws.org/Vol-3462/

• We have invited 2 distinguished keynote speakers.
• We have accepted 8 papers from 15 submissions.
• The deadline has been extended to June 1st.
• The QDB’26 workshop proposal has been accepted as a VLDB workshop.

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This workshop aims to exchange novel ideas and best practices about data quality assessment and improvement in the era of AI. The event should unite experienced and senior-level data quality researchers with junior researchers and PhD students. We specifically expect junior researchers to benefit, since they get to meet the community and continue high-quality research on data quality. The suggested topics of interest include, but are not limited to:

Foundational DQ methods and assessment

• Data profiling for data quality measurement
• Statistical methods to detect erroneous data
• Data lineage and provenance tracking
• Industry-specific data quality standards and compliance
• Data versioning and quality control
• Benchmark data sets to evaluate DQ assurance methods

AI/ML-specific data quality

• Data preparation
• Data quality for foundation models
• Data quality using generative AI
• Bias detection and mitigation in training data
• Data quality for few-shot and zero-shot learning
• Post-training quality/fact checking
• Explainable data cleaning
• ML-powered methods for improving data quality
• Integration of data quality tools in MLOps pipelines

Implementation and process optimization

• Automation of DQ assessment and improvement methods
• Real-time data quality monitoring
• Cost-benefit analysis of data quality improvements
• FAIRness in data quality

We appreciate submissions on all these topics across domains (e.g., healthcare, mobility, production) and data models (e.g., graphs, time series).

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• Felix Biessmann (Berlin University of Applied Sciences and Technology, Germany)
• Antoon Bronselaer (Ghent University, Belgium)
• Lisa Ehrlinger (Hasso Plattner Institute, University of Potsdam, Germanu)
• Sandra Geisler (RWTH Aachen University, Germany)
• Hazar Harmouch (University of Amsterdam, Netherlands)
• Thomas Hütter (Software Competence Center Hagenberg, Austria)
• Sebastian Link (University of Auckland, New Zealand)
• Adriana Marotta (Universidad de la República, Uruguay)
• Maria Angela Pellegrino (University of Salerno, Italy)
• Eduardo Pena (UTFPR, Brazil)
• Romila Pradhan (Purdue University, USA)
• Anisa Rula (University of Brescia, Italy)
• Flavia Serra (Universidad de la República, Uruguay)
• Wolfram Wöß (Johannes Kepler University Linz, Austria)

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