SUM 2019 : Thirteenth International Conference on Scalable Uncertainty Management
Conference Series : Scalable Uncertainty Management
Call For Papers
SUM 2019: the 13th international conference on Scalable Uncertainty Management
The thirteenth international conference on Scalable Uncertainty Management (SUM 2019) will be held in Compiègne (France), on December 16-18, 2019, see: https://sum2019.hds.utc.fr
The SUM conferences are annual events which gather researchers interested in dealing with imperfect information, in a wide range of fields such as artificial intelligence, databases, information retrieval, machine learning, and risk analysis, with the aim of fostering collaboration and cross-fertilization of ideas from different communities.
An originality of the SUM conferences is the care for dedicating a large part of their programs to tutorials covering 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:
The scope of the conference covers a wide range of topics related to the management of large amounts of information, in particular information of a complex kind, uncertain, incomplete, or inconsistent. 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 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
- 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
- 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
* Risk analysis *
- Aleatory vs. epistemic uncertainty *
- Uncertainty elicitation methods
- Uncertainty propagation methods
- Decision analysis methods
- Tools for synthesizing results
- Cassio P. de Campos (Utrecht University, Utrecht, the Netherlands): "Scalable reliable machine learning using sum-product networks"
- Jérôme Lang (Université Paris Dauphine, Paris, France): "Computational social choice (provisional title)"
- Wolfgang Gatterbauer (Northeastern Polytechnical University, Boston, MA, USA): "Algebraic approximations of the Probability of Boolean Functions (provisional title)"
- June 15th, 2019: submission deadline
- Sept. 1st, 2019: notification of acceptance
- Sept. 15th, 2019: camera-ready copies
- Dec. 16-18, 2019: conference
Submission guidelines and proceedings:
SUM 2019 solicits papers in the following three categories
- Long papers: technical papers reporting original research or survey papers
- Short papers: papers reporting promising work-in-progress, system descriptions, position papers on controversial issues, or survey papers providing a synthesis of some current research trends
- Extended abstracts of recently published work in a relevant journal or top-tier conference
Regular research papers should be at most 14 pages (including references, figures, and tables). Short papers should be between 4 and 7 pages. Extended abstracts should be at most 2 pages and should reference the originally published work.
Accepted long and short papers will be published by Springer in the Lecture Notes in Artificial Intelligence (LNAI) series. Authors of an accepted long or short paper will be expected to sign copyright release forms, and one author is expected to give a presentation at the conference. Authors of accepted abstracts will be expected to present their work during the conference, but the extended abstracts will not be published in the LNCS/LNAI proceedings (they will be made available in a separate booklet).
All submissions will be processed using EasyChair: https://easychair.org/conferences/?conf=sum2019
Submissions must be formatted according to Springer's guidelines for LNCS authors, which can be found at http://www.springer.de/comp/lncs/authors.html. Papers not respecting the formatting instructions or page limits may be rejected without review.
Except for extended abstracts, submissions must be unpublished and must not be under submission elsewhere. All submitted papers will be reviewed by the Program Committee on the basis of technical quality, relevance, significance, and clarity.
Yonatan Carlos Carranza Alarcon, Université de Technologie de Compiègne, France
Sébastien Destercke, CNRS, Université de Technologie de Compiègne, France
Marie-Hélène Masson, Université de Picardie Jules-Verne, France
Benjamin Quost, Université de Technologie de Compiègne, France (conference chair)
David Savourey, Université de Technologie de Compiègne, France
Program committee chairs:
Nahla Ben Amor, Institut Supérieur de Gestion de Tunis, Tunisia
Martin Theobald, University of Luxembourg, Luxembourg
Didier Dubois, IRIT-CNRS, France
Lluis Godo, IIIA-CSIC, Spain
Eyke Hüllermeier, Universität Paderborn, Germany
Anthony Hunter, University College London, UK
Henri Prade, IRIT-CNRS, France
Steven Schockaert, Cardiff University, UK
V. S. Subrahmanian, University of Maryland, USA