7. Schema Mapping Evolution Through Composition and Inversion

Mappings between different representations of data are the essential building blocks for many information integration tasks. A schema mapping is a high-level specification of the relationship between two schemas, and represents a useful abstraction that specifies how the data from a source format can be transformed into a target format. The development of schema mappings is laborious and time consuming, even in the presence of tools that facilitate this development. At the same time, schema evolution inevitably causes the invalidation of the existing schema mappings (since their schemas change). Providing tools and methods that can facilitate the adaptation and reuse of the existing schema mappings in the context of the new schemas is an important research problem. In this chapter, we show how two fundamental operators on schema mappings, namely composition and inversion, can be used to address the mapping adaptation problem in the context of schema evolution. We illustrate the applicability of the two operators in various concrete schema evolution scenarios, and we survey the most important developments on the semantics, algorithms, and implementation of composition and inversion. We also discuss the main research questions that still remain to be addressed.