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In many real applications, data are intrinsically uncertain due to measurement errors, interpretability issues, information incompleteness, etc. In those uncertain databases, users usually express quality requirements when the system evaluates their queries. However, as they may not be familiar with the contents of the queried database, their queries may be failing i.e., they may return no results or results that do not satisfy the expected degree of certainty. To provide users with relevant information in order to obtain alternative satisfactory results, we introduce a cooperative approach based on the dualization concept. This approach computes a set of meaningful subqueries (MFSs and XSSs) of the initial failing query, which is of paramount importance for query reformulation and relaxation purposes. The conducted experiments show that our proposition, a Mixed Dualization Matrix-Based approach (MDMB), outperforms existing algorithms, especially for large queries.

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