Instance based Matching using Regular Expression

A Survey of Schema Matching Research using Database Schemas and Instances

“…There are three types of information that commonly used to solve the problem of schema matching by identifying the semantic of schema attributes and detect the correspondences between database schemas, i.e., 1) schema information; 2) instances; and 3) auxiliary information [9], [10]. Several solutions have been proposed aiming at handling schema matching based on the available schema information [12], [13].…”

“…There are many beneficial levels of information that can be utilized to identify the schema matching. This includes metadata level, instance level, and auxiliary level [2], [10]. Apparently, several approaches have been proposed employed levels of schema information.…”

“…Apparently, several approaches have been proposed employed levels of schema information. Some of these approaches rely on utilizing each level independently as identified individual matcher based on their problematic situations and information available [10], [15]. While, other approaches involve a combination of the individual matcher to enhance the matching results [7], [16].…”

“…In many cases, it is not easy to obtain information from the schema structure as either it is not available or the information is meaningless and could not be used for the matching purpose [5], [10], [17]. Thus, in such cases, instances are considered as the most efficient and reliable source of information to identify the correspondences between attributes and determine the similarities and corresponding attributes of schema based on exploiting the characters of available values/instances.…”

“…It can be observed that Address.ZIP CustomerAddress.PostalCode represents an atomic-level schema matching between S1 and S2 elements [10], [11]. Element level matching also focuses on exploiting two levels that are linguistic matcher and constraint matchers.…”