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MoDU 2023 : Management of Data under Uncertainty

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Link: https://modu2023.lis-lab.fr/
 
When Sep 4, 2023 - Sep 4, 2023
Where Barcelona
Submission Deadline Apr 24, 2023
Notification Due May 15, 2023
Final Version Due Jun 9, 2023
Categories    data management   machine learning   data analytics
 

Call For Papers

Workshop on Management of Data under Uncertainty (MoDU 2023)
To be held in conjunction with ADBIS 2023 (https://www.essi.upc.edu/dtim/ADBIS2023/)
September 4, 2023
Barcelona, Spain
https://modu2023.lis-lab.fr/

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** GENERAL INFORMATION **
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 **
The aim of the workshop is to allow academics and industrial from various areas to share their experiences on management of uncertain data. Authors are encouraged to submit their research work and case studies addressing all facets of uncertainty and showing the usefulness of their approaches in different application domains.


** 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 **
Submissions should present original works not currently under review or published elsewhere. This workshop accepts research and industrial papers (regular: 12 pages max., and short: 6-8 pages max.) including novel approaches for management of uncertain data. All accepted papers will be published in the conference proceedings and will be submitted for inclusion. Papers should submitted in PDF format using the EasyChair online submission system (https://easychair.org/my/conference?conf=modu2023). Authors should consult Springer's authors' guidelines and use their proceedings templates, either for LaTeX or for Word, for the preparation of their papers.

** IMPORTANT DATES **
• Paper submission: April 24, 2023
• Notification of paper acceptance: May 15, 2023
• Camera-ready due: June 9, 2023
• Workshop day: September 4, 2023

** WORKSHOP ORGANIZERS **
• Richard Chbeir, University Pau & Pays Adour, Anglet, France
• Allel Hadjali, Engineer School ENSMA, Poitiers, France
• Sana Sellami, Aix Marseille University, Marseille, France

** PUBLICATION **
The workshop papers will be published by Springer in Communications in Computer and Information Science (CCIS). The authors of selected workshop papers will be invited to submit an extended version of their contributions to a special issue of a well-established and ranked international journal (tbd).

** DIVERSITY, EQUITY AND INCLUSION STATEMENT **
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.

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