Approximate functional dependencies are used in many emerging application domains, such as the identification of data inconsistencies or patterns of semantically related data, query rewriting, and so forth. They can approximate the canonical definition of functional dependency (FD) by relaxing on the data comparison (i.e., by considering data similarity rather than equality), on the extent (i.e., by admitting the possibility that the dependency holds on a subset of data), or both. Approximate FDs are difficult to be identified at design time like it happens with FDs. In this paper, we propose a genetic algorithm to discover approximate FDs from data. An empirical evaluation demonstrates the effectiveness of the algorithm.
A genetic algorithm to discover relaxed functional dependencies from data
Loredana Caruccio;Vincenzo Deufemia;Giuseppe Polese
2017-01-01
Abstract
Approximate functional dependencies are used in many emerging application domains, such as the identification of data inconsistencies or patterns of semantically related data, query rewriting, and so forth. They can approximate the canonical definition of functional dependency (FD) by relaxing on the data comparison (i.e., by considering data similarity rather than equality), on the extent (i.e., by admitting the possibility that the dependency holds on a subset of data), or both. Approximate FDs are difficult to be identified at design time like it happens with FDs. In this paper, we propose a genetic algorithm to discover approximate FDs from data. An empirical evaluation demonstrates the effectiveness of the algorithm.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
