QDB: Quality in Databases

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Past:   Proceedings on DBLP

Future:  Post a CFP for 2024 or later   |   Invite the Organizers Email

 
 

All CFPs on WikiCFP

Event When Where Deadline
QDB 2023 12th International Workshop on Quality in Databases (QDB’23) @ VLDB 2023
Aug 28, 2023 - Aug 28, 2023 Vancouver, Canada May 31, 2023
QDB 2016 VLDB Workshop on Quality in Databases
Sep 5, 2016 - Sep 5, 2016 New Delhi, India Jun 3, 2016
QDB 2013 11th International Workshop on Quality in DataBases
Aug 26, 2013 - Aug 26, 2013 Trento, Italy May 30, 2013
QDB 2012 10th International Workshop on Quality in DataBases
Aug 27, 2012 - Aug 27, 2012 Istanbul, Turkey May 30, 2012
QDB 2011 9th International Workshop on Quality in Databases
Aug 29, 2011 - Aug 29, 2011 Seattle, WA, USA Jun 4, 2011
QDB 2008 6th International Workshop on Quality in Databases
Aug 25, 2008 - Aug 25, 2008 Auckland, New Zealand May 26, 2008
 
 

Present CFP : 2023

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CALL FOR PAPERS

12th International Workshop on Quality in Databases (QDB'23)
In conjunction with VLDB 2023
August 28 (Monday), 2023, Vancouver, Canada Submissions due: May 31, 2023
https://hpi.de/naumann/s/qdb2023.html
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** Aims of the Workshop **
Data quality has been a major concern of organizations for decades. The recent advances in artificial intelligence (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.

QDB'23 revives the successful QDB workshop series to cover the needs of the AI era, addressing both industry and academia (cf. data-centric AI). The workshop aims to (1) revive vital discussions about data quality, and (2) specifically exchange novel ideas and best practices about 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 quality assessment for AI-based systems
- Data quality improvement / data cleaning for AI-based systems
- Data preprocessing and data preparation
- Benchmark data sets to evaluate DQ assurance methods
- Automation of DQ assessment and improvement methods
- ML-powered methods for improving data quality
- Data profiling for data quality measurement
- Data quality in graph-structured or time-series data
- Metadata management
- Human-in-the-loop approaches for DQ
- Post-training quality / fact checking
- Explainable data cleaning
- Methods to scale data quality assessment and cleansing
- FAIRness in data quality


** Workshop Chairs **
- Lisa Ehrlinger, Software Competence Center Hagenberg GmbH, Austria
- Hazar Harmouch, Hasso Plattner Institute, University of Potsdam, Germany
- Ihab Ilyas, Apple, University of Waterloo, USA
- Felix Naumann, Hasso Plattner Institute, University of Potsdam, Germany

** Important Dates **
Submission: May 31, 2023
Notification: July 25, 2023
CRC: August 10, 2023
QDB workshop: August 28, 2023

** Submission **
Authors are invited to submit original, unpublished full research papers and demo descriptions that are not being considered for publication in any other forum.
Please submit your paper as a PDF using Microsoft's QDB CMT site: https://cmt3.research.microsoft.com/QDB2023
You need to append the category tag as a suffix to the title of the paper, such as “Data Management in the Year 3000 [Regular]”; “Spatial Database System [Demo]”. This must be done both in the paper file and in the CMT submission title. The suffix will not be part of the camera-ready copy if the paper is accepted.
 

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