Call for Papers

HomeCall for Papers

The 14th International Conference on Scalable Uncertainty Management (SUM 2020) will be  held from September 23-25, 2020. Originally planned to be held in Bolzano, Italy, due to the ongoing COVID-19 situation we are planning on holding the conference virtually. There will still be an official Springer proceedings.

Established in 2007, the SUM conferences are annual events which 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, 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 14th International Conference on Scalable Uncertainty Management (SUM 2020) will be held in Bolzano, Italy from September 23-25, 2020.

New PhD Track

A special feature of this 2020 edition of SUM conference is to introduce a special PhD track. Thus, we strongly encourage PhD students to participate in this track to benefit from feedback at an early stage. As regular submissions, reviewers will review the submissions and accepted ones will be presented at the conference and published by in the Springer Proceedings of the SUM2020 conference. To be considered for this track, the first author must be a PhD student and must inform the program co-chairs by email that his or her submission should be considered for this track.

The submissions to the PhD track are the same as the other submissions and can therefore be long, short or just extended abstracts and they must be formatted according to the LNCS/LNAI guidelines.

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

Submission Guidelines

SUM 2020 solicits original papers in the following three categories:

  • Long papers (14 pages): technical papers reporting original research or survey papers
  • Short papers (8 pages): 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 (2 pages) of recently published work in a relevant journal or top-tier conference

All SUM submissions must be formatted according to the LNCS/LNAI guidelines: https://www.springer.com/gp/computer-science/lncs/conference-proceedings-guidelines

Papers should be submitted via EasyChair:

https://easychair.org/conferences/?conf=sum2020

Dates

All Deadlines are 23:59 Central European Time. 

  • June 5th, 2020: Submission deadline
  • July 7th, 2020: Notification
  • July 14th, 2020: Camera-ready copies due
  • Sept. 23rd-25th, 2020: Conference

Publication

Accepted long (14 pages) and short papers (8 pages) 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 (2 pages) 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)

PC Chairs

  • Jesse Davis, Katholieke Universiteit Leuven
  • Karim Tabia, Université d’Artois

PC Members

  • Alessandro Antonucci, IDSIA
  • Nahla Ben Amor, Institut Supérieur de Gestion de Tunis
  • Salem Benferhat, Cril CNRS UMR8188, Université d’Artois
  • Leopoldo Bertossi, Adolfo Ibáñez University (Santiago, Chile)
  • Fernando Bobillo, University of Zaragoza
  • Imen Boukhris, LARODEC – Université de Tunis- ISG Tunis
  • Davide Ciucci, Università di Milano-Bicocca   
  • Thierry Denoeux, Université de Technologie de Compiègne
  • Sébastien Destercke, CNRS UMR Heudiasyc
  • Zied Elouedi, Institut Supérieur de Gestion de Tunis
  • Rainer Gemulla, Universität Mannheim
  • Lluis Godo, Artificial Intelligence Research Institute, IIIA – CSIC   
  • John Grant, Towson University
  • Manuel Gómez-Olmedo, University of Granada
  • Arjen Hommersom, Open University of the Netherlands
  • Angelika Kimmig, Cardiff University
  • Eric Lefevre, Université d’Artois
  • Philippe Leray, LS2N/DUKe – Nantes University
  • Sebastian Link, The University of Auckland
  • Thomas Lukasiewicz, University of Oxford
  • Silviu Maniu, Universite Paris-Sud
  • Serafin Moral, University of Granada
  • Francesco Parisi, DIMES – University of Calabria
  • Nico Potyka, Universitaet Osnabrueck, IKW
  • Henri Prade, IRIT – CNRS
  • Andrea Pugliese, University of Calabria
  • Benjamin Quost, HeuDiaSyC laboratory, University of Technology of Compiègne
  • Steven Schockaert, Cardiff University
  • Umberto Straccia, ISTI-CNR     
  • Andrea Tettamanzi, Univ. Nice Sophia Antipolis
  • Matthias Thimm, Universität Koblenz-Landau
  • Barbara Vantaggi, Universita’ La Sapienza of Rome
  • Maurice van Keulen, University of Twente