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(Jul
5) Registration
is now open
The conference
program is now available
The conference
proceedings are now available online through SpringerLink
There is now intense interest in both the Artificial
Intelligence community and in the Database community on managing
uncertainty and inconsistency in databases. However, researchers in AI may
not necessarily be aware of database concerns and conversely, database
researchers may not always be aware of AI concerns and past contributions.
There is no single, unified research community that addresses the problems
of managing huge amounts of uncertainty and inconsistency in a semantically
defendable manner when huge amounts of data are being processed.
In order to help create such a community, we start a
3-day international conference on "Scalable Uncertainty
Management" (SUM). The first SUM conference will be held in the Washington DC
area. We expect this conference to continue as an annual series. The
conference is intended to focus on the following broad areas:
1. Uncertainty
models, probabilistic logics, fuzzy logics, annotated logics, possibilistic
logics, Bayesian models, Markov models and others.
2. Inconsistency
logics. multiple valued inconsistency models, default and preferential
logics, logics of inconsistency, paraconsistent logics.
3. Database
algebras and calculi. extensions of the relational, object oriented,
semi-structured, semantic web and other related algebras and calculi to
incorporate one or more uncertainty/inconsistency models.
4. Scalable
database systems incorporating uncertainty. Query optimization techniques,
indexing methods, aggregate query processing techniques, view management
techniques in databases that include uncertainty and inconsistency
management.
5. Spatial,
temporal, mobile and multimedia databases incorporating uncertainty.
Techniques to scalably reason about the inconsistency and/or uncertainty
that is inherently present in temporal, geospatial, mobile and multimedia databases.
6. Implementations.
Descriptions of implemented systems with a focus on experimental methods
(and perhaps a mix of heuristic and exact algorithms) that show scalable
performance.
7. Applications.
Novel applications of implemented systems that reason about either large
amounts of uncertainty or inconsistency in large, real world data sets.
Applications can span areas such as computer vision, audio and speech
processing, to industrial applications and case studies of how companies
handled these problems.
General Chair:
Didier
Dubois (Univ. Paul Sabatier, France)
Program Chairs:
Henri
Prade (Univ. Paul Sabatier, France)
V.S.
Subrahmanian (Univ. of Maryland,
USA)
Publicity Chair:
Andrea
Pugliese (Univ. of Calabria,
Italy)
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