Data dependencies play important roles in database design, data
quality management and knowledge representation. Dependencies in
knowledge discovery are extracted from the existing data of the
database. This extraction process is called dependency discovery.
The aim of dependency discovery is to find important
dependencies holding on the data of the database. These discovered
dependencies represent domain knowledge and can be used to verify
database design and assess data quality.
Dependency discovery has attracted a lot of research interests
from scientists since early 1980s. At the present time, the problem of
discovering data dependencies on big data sets becomes more
important because these big data sets contain a lot of valuable
knowledge.
Currently, with the development of digital devices, especially
social networks and smart phone applications, the amount of data in
the applications increases very quickly, these arise proplems in data
storage, data management, especially the problem of knowledge
discovery from those big data sets. The problem of discovering FDs
and RFDs in databases is one of important proplems of knowledge
discovery. Three typical types of data dependencies which are
interested in discovering are FD, AFD and CFD. AFD is an
extension of FD, the “approximation” is based on a degree of
satisfaction or an error measure; CFD is an extension of FD which
aims to capture inconsistencies in data.