[CfP] 14th International Workshop on Quality in Databases (QDB'25), co-located with VLDB2025

*******************************************************
CALL FOR PAPERS

14th International Workshop on Quality in Databases (QDB'25)
In conjunction with VLDB 2025
September 1 (Monday), 2025, London, UK
Submissions due: May 16, 2025
https://qdb-workshop.github.io/

*******************************************************

** Aims of the Workshop **

Data quality has been a significant 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 spotlighted data quality (DQ). Building data ecosystems that can cope with the emerging challenges posed by AI-based systems is crucial in enterprises. 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. It is essential to bring together these communities to foster a vital discussion about the future of DQ assessment and improvement.

The International Workshop on Quality in Databases (QDB) has experienced significant success in its last two editions (2023 and 2024), attracting many participants. QDB provides an excellent opportunity for the community to come together and exchange new ideas, especially for PhD students and junior researchers who may be uncertain about navigating their niche within the field.

This workshop focuses on sharing innovative ideas and best practices related to data quality assessment and improvement in the era of AI. It aims to bring together experienced, senior-level data quality researchers with junior researchers and PhD students. We expect junior researchers to benefit significantly from this event, as it will allow them to connect with the community and pursue high-quality research in data quality.


** Topics of Interest **

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 preprocessing
• 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
• FAIRness in data quality, according to the FAIR principles ( see https://www.go-fair.org/fair-principles)
• Explainable data cleaning
• ML-powered methods for improving data quality

**Implementation and process optimization**
• Automation of DQ assessment and improvement methods
• Real-time data quality monitoring
• Cost-benefit analysis of data quality improvements
• Integration of data quality tools in MLOps pipelines

** Workshop Chairs **
- Sourav S Bhowmick, Nanyang Technological University, Singapore
- Lisa Ehrlinger, Hasso Plattner Institute, University of Potsdam, Germany
- Lorena Etcheverry, Universidad de la República, Uruguay
- Hazar Harmouch, University of Amsterdam, Netherlands

** Important Dates **
Submission: May 16, 2025
Notification: June 20, 2025
CRC: July 8, 2025
QDB workshop: September 1, 2025

** Submission **
Authors are invited to submit original, unpublished research papers not being considered for publication in any other forum.
Manuscripts should be formatted and submitted as PDFs using VLDB format. The template can be found at  https://github.com/haixun/haixun.github.io/blob/master/vldb/vldb-workshop-style-master.zip

Full research papers and demo descriptions can be up to 6 pages long, including all figures, tables, and references. The submission site is https://cmt3.research.microsoft.com/QDB2025