Conference Information

SUM 2018: International Conference on Scalable Uncertainty Management

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Submission Date:
2018-04-01
Notification Date:
2018-05-20
Conference Date:
2018-10-03
Location:
Milan, Italy
Years:
12
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Call For Papers

SUM 2018 (International Conference on Scalable Uncertainty Management) is an academic conference held in Milan, Italy on 2018-10-03. The paper submission deadline is 2018-04-01. Acceptance notifications are sent on 2018-05-20.

The 12th International Conference on Scalable Uncertainty Management (SUM) will be held in Milano, Italy on October 3-5, 2018. The conference will bring together researchers who are working with imperfect information in fields such as artificial intelligence, databases, data mining, information retrieval, and risk analysis with the aim of fostering collaboration and cross-fertilization of ideas from different communities. An originality of SUM is giving a large space to tutorials about a wide range of topics related to uncertainty management. Each tutorial provides a 45-minute survey of one of the research areas in the scope of the conference. Topics of interest 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
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