Call for Papers
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. The Scalable Uncertainty Modeling conference aims to
bring all those interested in the management of large volumes of
uncertainty together, irrespective of whether they are in databases
or in AI. Papers are solicited in all areas of massive uncertainty
including, but not restricted to, the topics listed below.
Probability Logic
|
Fuzzy Logic
|
Annotated Logic
|
Bayesian Models
|
Markov Models
|
Paraconsistent
Logic
|
Uncertain DB
Algebras
|
Query
Optimization with Uncertainty |
Spatio-temporal
Uncertainty |
| Uncertain
Aggregate Queries |
Caching for
Uncertain DBs
|
Inconsistency
Management |
| Indexing methods
for Uncertainty |
View Management
|
Uncertain
Aggregate Queries |
| Multimedia &
Uncertainty |
Mobile Systems
|
Implementations |
| Applications |
Vision and
Uncertainty
|
Audio Processing
and Uncertainty
|
| Semantics of
Uncertain Data |
Formal Models of
Uncertain data |
Reasoning with
Incosistent and Uncertain Information |
| Inconsistency
Management |
Uncertainty and
Views |
Mobile
Uncertainty |
| Data Mining |
Learning |
Uncertain DB
Calculi |
| Data Sharing and
Uncertainty |
Approximate
Schema Mapping |
Commercial
Systems |
The proceedings of the conference
will be published by Springer Verlag in their
Lecture Notes in AI Series.
Research papers of 14 pages maximum in the Springer Lecture notes in
Computer Science format (www.springer.com/lncs)
are sought.
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