Conference Information
SUM 2018: International Conference on Scalable Uncertainty Management
http://www.ir.disco.unimib.it/sum2018/
Submission Date:
2018-04-01
Notification Date:
2018-05-20
Conference Date:
2018-10-03
Location:
Milan, Italy
Years:
12
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Conference Location
Call For Papers
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
Last updated by Dou Sun in 2018-04-07
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