Granular approach to data processing under probabilistic uncertainty

The existing algorithms for data processing under probabilistic uncertainty often require too much computation time. Sometimes, we can speed up the corresponding computations if we take into account the fact that in many real-life situations, uncertainty can be naturally described as a combination of several components, components which are described by different granules. In such situations, to process this uncertainty, it is often beneficial to take this granularity into account by processing these granules separately and then combining the results. In this paper, we show that granular computing can help even in situations when there is no such natural decomposition into granules, namely we can often speed up processing of uncertainty if we first (artificially) decompose the original uncertainty into appropriate granules.

Automated summary

The algorithms for data processing under probabilistic uncertainty often require too much computation time. This study shows that processing uncertainty can be sped up by decomposing the original uncertainty into appropriate granules, even in situations where there is no natural decomposition.