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About MoDU 2023
In the era of big data and Machine learning, the issue of uncertainty, is still an active research topic. Uncertainty needs to be managed at various levels of data management process: data collection, data querying, machine learning and data analytics. For instance, the presence of uncertainty can be source of semantics errors during query evaluation. Moreover, traditional machine learning and deep learning models do not consider uncertainty in data and predictions while they are prone to noises. Then, quantifying uncertainty is a critical challenge for most machine learning techniques. From analytics perspective, the presence of uncertainty and imprecision can cause inaccuracies in predictions that may impact the quality of the data analytics procedures themselves.
Goal of the Workshop
List of Topics
We seek contributions covering all aspects of data management under uncertainty, including, but not limited to, the following topics:
Uncertainty in data collection and querying:
• Incompleteness, ambiguity, inconsistency in data
• Aleatoric and epistemic uncertainty
• Uncertainty Modeling
• Querying uncertain data
• Approximate query
• Uncertain data fusion
Uncertainty in ML and Deep learning models:
• Prediction models for uncertain data
• Uncertainty quantification in deep and machine learning
• Uncertainty in data labeling
• Reasoning under uncertainty
• Uncertain data and conformal approaches
Uncertainty in data analytics:
• Uncertainty and Imprecision in (big) data management and Analytics
• Uncertain Spatial Data Management
• Data mining on uncertain data
• Metrics for uncertainty and data quality
• Uncertainty quantification
Submission Instructions
Important Dates
Publication
Keynote Speaker
Laure Berti-Équille
Research Director (DR1) at IRD
Abstract: Uncertainty Quantification and Other Challenges in Applied ML
Diversity, Equity and Inclusion
We kindly ask authors to adopt inclusive language in their papers and presentations. Our aim is to promote DEI in MoDU by implementing different actions such as having diverse PC members, co-chairs and session chairpersons.
