The 16th International Conference on Scalable Uncertainty Management (SUM 2024)

November 27-29, 2024, Palermo, Italy

Established in 2007, the SUM conferences aim to gather researchers with a common interest in managing and analyzing imperfect information from a wide range of fields, such as Artificial Intelligence and Machine Learning, Uncertain reasoning, Databases, Information Retrieval and Data Mining, the Semantic Web and Risk Analysis, and with the aim of fostering collaboration and cross-fertilization of ideas from the different communities.

An originality of the SUM conferences is their care for dedicating a large space of their program to tutorials covering a wide range of topics related to uncertainty management. Each tutorial provides a survey of one of the research areas in the scope of the conference.

The SUM conferences were originally held annually. However, starting from 2020, they became biennial events, occurring every two years. The first SUM conference was held in Washington DC in 2007. Since then, the SUM conferences have successively taken place in Naples (2008), Washington DC (2009), Toulouse (2010), Dayton, (2011), Marburg (2012), Washington DC (2013), Oxford (2014), Québec (2015), Nizza (2016), Granada (2017), Milan (2018), Compiègne (2019), Bolzano (2020), Paris (2022).

The 16th International Conference on Scalable Uncertainty Management (SUM) will be held in Palermo, Italy on November 27-29, 2024.

We solicit papers on the management of large amounts of complex kinds of uncertain, incomplete, or inconsistent information. We are particularly interested in papers that focus on bridging gaps, for instance between different communities, between numerical and symbolic approaches, or between theory and practice. Topics of interest include (but are not limited to):

Imperfect information in databases
Methods for modeling, indexing, and querying uncertain databases
Top-k queries, skyline query processing, and ranking
Approximate, fuzzy query processing
Uncertainty in data integration and exchange
Uncertainty and imprecision in geographic information systems
Probabilistic databases and possibilistic databases?
Data provenance and trust
Data summarization
Very large datasets
Imperfect information in information retrieval and semantic web applications
Approximate schema and ontology matching
Uncertainty in description logics and logic programming
Learning to rank, personalization, and user preferences
Probabilistic language models
Combining vector-space models with symbolic representations
Inductive reasoning for the semantic web
Imperfect information in artificial intelligence
Statistical relational learning, graphical models, probabilistic inference
Argumentation, defeasible reasoning, belief revision
Weighted logics for managing uncertainty
Reasoning with imprecise probability, Dempster-Shafer theory, possibility theory
Approximate reasoning, similarity-based reasoning, analogical reasoning
Planning under uncertainty, reasoning about actions, spatial and temporal reasoning
Incomplete preference specifications
Learning from data
Risk analysis
Aleatory vs. epistemic uncertainty
Uncertainty elicitation methods
Uncertainty propagation methods
Decision analysis methods
Tools for synthesizing results