One of the most difficult issues today, is the integration of data from various sources. Thus, it
arises the need of automatic Data Integration (DI) methods. However, in the literature there …

Since data is often stored in different sources, it needs to be integrated to gather a global
view that is required in order to create value and derive knowledge from it. A critical step in …

Schema matching is considered as one of the essential phases of data integration in
database systems. The main aim of the schema matching process is to identify the …

The knowledge of interschema properties (eg, synonymies, homonymies, hyponymies and
subschema similarities) plays a key role for allowing decision-making in sources …

Instance based schema matching is to determine the correspondences between
heterogeneous databases by comparing instances. This process is especially effective …

Instance-based schema matching is to determine the correspondences between
heterogeneous databases by comparing instances. Heterogeneous databases consist of an …

This paper experimentally evaluates the instance-based schema matching method for the
attributes storing numerical data. This method uses data distributions and correlations …

Current practice in data integration typically requires extensive upfront effort, such as
defining a schema mapping before a useful result can be produced. Dataspace is …

Identification of attributes in heterogeneous databases is widely known as the schema
matching problem, and many studies have been published. Although existing studies using …

Entity resolution is an important step in data cleaning and data integration, which can
identify records from different data sources that refer to the same entity. For the problem of …