Incremental Discovery of Imprecise Functional Dependencies

Functional dependencies (FDs) are one of the metadata used to assess data quality and to perform data cleaning operations. However, in order to pursue robustness with respect to data errors, it has been necessary to devise imprecise versions of functional dependencies, yielding relaxed functional dependencies (RFDs). Among them, there exists the class of RFDs relaxing on the extent, i.e., those admitting the possibility that an FD holds on a subset of data. In the literature several algorithms to automatically discover RFDs from big data collections have been defined. They achieve good performances with respect to the inherent problem complexity. However, most of them are capable of discovering RFDs only by batch processing the entire dataset. This is not suitable in the era of big data, where the size of a database instance can grow with high-velocity, and the insertion of new data can invalidate previously holding RFDs. Thus, it is necessary to devise incremental discovery algorithms capable of updating the set of holding RFDs upon data insertions, without processing the entire dataset. To this end, in this paper we propose an incremental discovery algorithm for RFDs relaxing on the extent. It manages the validation of candidate RFDs and the generation of possibly new RFD candidates upon the insertion of the new tuples, while limiting the size of the overall search space. Experimental results show that the proposed algorithm achieves extremely good performances on real-world datasets.