[CfP] 13th International Workshop on Quality in Databases (QDB'24), co-located with VLDB 2024

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

CALL FOR PAPERS


13th International Workshop on Quality in Databases (QDB'24) 

In conjunction with VLDB 2024

August 25 (Sunday), 2024, Guangzhou, China 

Submissions due: May 31, 2024

https://hpi.de/naumann/s/qdb2024.html 

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


** Aims of the Workshop **

Data quality has been a major concern of organizations for decades. The
recent advances in artificial intelligence (AI), e.g.,
generative AI, have brought data quality (DQ) back into the spotlight.
While many recent data quality and cleaning solutions are
powered by ML, DQ is a core requirement to ensure reliable AI-based
systems. DQ is tackled from different perspectives by
different research communities, including database, machine learning
(ML), and information systems. We believe it is important to
bring together these communities to foster a vital discussion about the
future of DQ assessment and improvement.


Considering the large number of participants (>50) at QDB’23
[https://hpi.de/naumann/projects/conferences-and-workshops-hosted/qdb-2023.html],
QDB'24 aims to (1) continue the vital
discussions about data quality, and (2) share best practices and novel
methods for (semi-)automated (ML-based) data quality
assessment and improvement in the context of AI-based systems. 


** Topics of Interest **

The focus is on new and practical methods for (semi-)automated
(ML-based) data quality assessment and improvement. The topics of
interest include, but are not limited to:

- Data preprocessing

- Data profiling for data quality measurement

- Explainable data cleaning

- DQ requirements for generative AI systems

- DQ using generative AI

- Data quality assessment for AI-based systems

- Data quality improvement / data cleaning for AI-based systems

- Benchmark data sets to evaluate DQ assurance methods

- Automation of DQ assessment and improvement methods

- Methods to scale data quality assessment and cleansing

- ML-powered methods for improving data quality

- Data quality in graph-structured or time-series data

- Metadata management to improve data quality

- Data quality in different data science domains

- Human-in-the-loop approaches for DQ

- Post-training quality / fact checking

- FAIRness in data quality


** Workshop Chairs **

- Sourav S Bhowmick, Nanyang Technological University, Singapore

- Lisa Ehrlinger, Software Competence Center Hagenberg GmbH, Austria

- Hazar Harmouch, University of Amsterdam, Netherlands


** Important Dates **

Submission: May 31, 2024

Notification: July 25, 2024

CRC: August 10, 2024

QDB workshop: August 25, 2024


** Submission **

Authors are invited to submit original, unpublished research papers that
are not being considered for publication in any other
forum.

Manuscripts should be formatted and submitted as a PDF according to the
VLDB format. The template can be found at
https://vldb.org/pvldb/volumes/16/formatting.

Full research papers and demo descriptions can be up to 6 pages in
length, including all figures, tables, and references. The
submission site is https://cmt3.research.microsoft.com/QDB2024/
[https://cmt3.research.microsoft.com/QDB2024/]