In a paper published in the 2001 VLDB Conference, we proposed treating generic schema matching as an independent problem. We developed a taxonomy of existing techniques, a new schema matching algorithm, and an approach to comparative evaluation. Since then, the field has grown into a major research topic. We briefly summarize the new techniques that have been developed and applications of the t...

Data exchange between enterprise resource planning (ERP) systems in a supply chain system needs to fulfill requirements of both schema integration and message translation. Since ERP systems with relational database systems are developed independently, schema conflicts between databases is a common problem for schema integration. Thus, supply chain partners need to preserve the data integrity of...

Finding nontrivial semantic matches between database schemas 3 Summary Automation of schema matching has been under investigation for already some decades, still the systems usually do not find all matches or suggests incorrect matches. Due to this imperfection matching schemas it is still often done manually by domain experts. The rapidly increasing number of heterogeneous and distributed data...

Schema matching is the process of identifying semantic correspondences between schemas. COMA++ is a matching prototype which uses several characteristics of schemas to determine similarities between them, for example the names and data types of the schema elements and structural information. In this paper we propose two instance-based matchers for COMA++ to gain a further quality improvement. T...

Schematic and semantic heterogeneity are two important types of heterogeneities that need to be resolved in order to enable interoperability and exchange of data among distributed and heterogeneous databases in a collaborative network of biodiversity nodes. This paper describes the SASMINT system, which performs schema matching and integration among databases. SASMINT identifies syntactic/seman...

Under the Unique Name Assumption (UNA), users need to have shared agreements on signifiers to use in schema or data, e.g. to use “genre” and not “type” to refer to a movie’s category. Agreements are difficult in open environments such as datasets on the web, open data, and crowd-sourced databases, thus this assumption can be invalid. Schema matching and data integration can be limited in respon...