Exploiting Uncertain and Temporal Information in Correlation
This work addresses the challenge of handling uncertainty and temporal imprecision in information correlation, which is incremental as it builds on existing theories like possibilistic logic and probability.
The paper tackles the problem of correlating information with incomplete system models and imprecise temporal dependencies by introducing a modeling language, and outlines an efficient incremental implementation using cost functions that are satisfied by possibilistic logic and probability theory.
A modelling language is described which is suitable for the correlation of information when the underlying functional model of the system is incomplete or uncertain and the temporal dependencies are imprecise. An efficient and incremental implementation is outlined which depends on cost functions satisfying certain criteria. Possibilistic logic and probability theory (as it is used in the applications targetted) satisfy these criteria.