Schema Evolution publication categorizer

Attribute-level schema matching has proven to be an important first step in developing mappings for data exchange, integration,
restructuring and schema evolution. In this paper we investigate
contextual schema matching, in which selection conditions are associated
with matches by the schema matching process in order
to improve overall match quality. We define a general space of
matching techniques, and within this framework we identify a variety
of novel, concrete algorithms for contextual schema matching.
Furthermore, we show how common schema mapping techniques
can be generalized to take more effective advantage of contextual
matches, enabling automatic construction of mappings across certain
forms of schema heterogeneity. An experimental study examines
a wide variety of quality and performance issues. In addition, it
demonstrates that contextual schema matching is an effective and
practical technique to further automate the definition of complex
data transformations.