Sampling from repairs of conditional functional dependency violations

Violations of functional dependencies (FDs) and conditional functional dependencies (CFDs) are common in practice, often indicating deviations from the intended data semantics. These violations arise in many contexts such as data integration and Web data extraction. Resolving these violations is challenging for a variety of reasons, one of them being the exponential number of possible repairs. Most of the previous work has tackled this problem by producing a single repair that is nearly optimal with respect to some metric. In this paper, we propose a novel data cleaning approach that is not limited to finding a single repair, namely sampling from the space of possible repairs. We give several motivating scenarios where sampling from the space of CFD repairs is desirable, we propose a new class of useful repairs, and we present an algorithm that randomly samples from this space in an efficient way. We also show how to restrict the space of repairs based on constraints that reflect the accuracy of different parts of the database. We experimentally evaluate our algorithms against previous approaches to show the utility and efficiency of our approach.