FOX: Inference of Approximate Functional Dependencies from XML Data

Functional dependencies (FDs) are an integral part of relational database theory since they are used in integrity enforcement and in database design. Despite their importance FDs are often not specified or some of them are not expected by database designers, but they occur in the data and the need of inferring them from data arises. Furthermore, in several areas as data cleaning, data integration and data analysis, an important task is to find approximate functional dependencies (that are FDs approximately satisfied by a data collection) in order to discovery erroneous or exceptional elements in the data. In this work we present a system, called Fox, that infers approximate functional dependencies from XML documents employing a new notion of approximation suitable for XML data. Moreover, we show experimental results assessing the effectiveness of the Fox system and indicating that our approach is promising from the point of view of the semantic significance of the mined knowledge.