The International Conference on Scalable Uncertainty Management (SUM) is an annual conference that was launched in 2007 with the goal to exploit and strengthen the connection between the Artificial Intelligence and Database communities. It aims at bringing together all those researchers interested in the management of massive amounts of uncertain, incomplete or inconsistent information. Such information originates commonly in applications where significant computational effort is needed to process data in a meaningful and semantically justifiable manner. Typical applications of that kind include databases, the Web, and the life sciences.
In 2012, SUM will take place in Marburg, Germany. Apart from the regular topics within the scope of the conference, SUM-2012 seeks to specifically promote a certain number of special themes:
In 2012, SUM will take place in Marburg, Germany. Apart from the regular topics within the scope of the conference, SUM-2012 seeks to specifically promote a certain number of special themes:
- biological and medical data analysis (supported by Volker Roth)
- computational argumentation (supported by Guillermo Simari)
- computational preference analysis (supported by Ingo Schmitt)
- consistent query answering (supported by Jef Wijsen)
- evolving fuzzy systems and modeling (supported by Edwin Lughofer and Moamar Sayed-Mouchaweh)
- logic programming (supported by Steven Schokaert)
- managing preferences in web services retrieval (supported by Allel Hadjali and Djamal Benslimane)
- massive data streams (supported by Mohamed Gaber)
- ranking and uncertain data management (supported by Martin Theobald)
- scalable data mining (supported by Anne Laurent)
- uncertainty, inconsistency and incompleteness in security and privacy (supported by Lena Wiese)
- uncertainty, reliability and risk in engineering (supported by Michael Beer, Ioannis Kougioumtzoglou and Edoardo Patelli)
- visual analytics (supported by Daniel Keim)
Papers are solicited in all areas of managing and reasoning with substantial and complex kinds of uncertain, incomplete or inconsistent information. These include (but are not restricted to) applications in decision support systems, machine learning, negotiation technologies, semantic web applications, search engines, ontology systems, information retrieval, natural language processing, information extraction, image recognition, vision systems, data and text mining, and the consideration of issues such as provenance, trust, heterogeneity, and complexity of data and knowledge.
