{U}sing {S}chema {M}atching to {S}implify {H}eterogeneous {D}ata {T}ranslation

Abstract

A broad spectrum of data is available on the Web in distinct heterogeneoussources, and stored under different formats. As the number of systems that utilize this heterogeneous data grows, the importance of data translation and conversion mechanisms increases greatly. In this paper we present a new translation system, based on schema-matching, aimed at simplifying the intricate task of data conversion. We observe that in many cases the schema of the data in the source system is very similar to that of the target system. In such cases, much of the translation work can be done automatically, based on the schemas similarity. This saves a lot of effort for the user, limiting the amount of programming needed. We define common schema and data models, in which schemas and data (resp.)from many common models can be represented. Using a rule-based method, the source schema is compared with the target one, and each component in the source schema is matched with a corresponding component in the target schema. Then, based on the matching achieved, data instances of the source schema can be translated to instances of the target schema. We show that our schema-based translation system allows a convenient specification and customization of data conversions, and can be easily combinedwith the traditional data-based translation languages.