Consistent query answering (CQA) aims to deliver meaningful answers when queries are evaluated over inconsistent databases. Such answers must be certainly true in all repairs, which are consistent databases whose difference from the inconsistent one is somehow minimal. Although CQA provides a clean framework for querying inconsistent databases, it is arguably more informative to compute the percentage of repairs in which a candidate answer is true, instead of simply saying that is true in all repairs, or is false in at least one repair. It should not be surprising, though, that computing this percentage is computationally hard. On the other hand, for practically relevant settings such as conjunctive queries and primary keys, there are data-efficient randomized approximation schemes for approximating this percentage. Our goal is to perform a thorough experimental evaluation and comparison of those approximation schemes. Our analysis provides new insights on which technique is indicated depending on key characteristics of the input, and it further provides evidence that making approximate CQA as described above feasible in practice is not an unrealistic goal.
2021, PODS'21: Proceedings of the 40th ACM SIGMOD-SIGACT-SIGAI Symposium on Principles of Database Systems, Pages 233-246
Benchmarking approximate consistent query answering (04b Atto di convegno in volume)
Calautti M., Console M., Pieris A.
Gruppo di ricerca: Artificial Intelligence and Knowledge Representation