Exploiting Uncertain and Temporal Information in Correlation

TL;DR

This paper introduces a modeling language designed to handle uncertain, incomplete, and imprecise temporal information in correlation tasks, with an efficient implementation based on cost functions.

Contribution

It presents a novel modeling language and an incremental implementation that effectively manages uncertain and temporal data using possibilistic logic and probability theory.

Findings

Efficient incremental implementation demonstrated

Applicable to systems with incomplete or uncertain models

Utilizes cost functions satisfying specific criteria

Abstract

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.