Reuse of Schema Mappings for Data Transformation Design

The definition of data transformations between heterogeneous schemas is a critical activity of any database application. Currently, automated tools provide high level interfaces for the discovery of correspondences between elements of schemas, but transformations (i.e., schema mappings) need to be manually specified every time from scratch, even if the problem at hand is similar to one that has already been addressed. Schema mappings are precisely defined over a pair of schemas and cannot directly be reused on different scenarios. We tackle this challenge by introducing a framework to generalize schema mappings as meta-mappings : formalisms that describe transformations between generic data structures called meta-schemas. Meta-mappings describe, in abstract terms, database transformations and are suitable to capture enterprise knowledge from previously defined schema mappings. We present novel techniques to infer meta-mappings from schema mappings, to organize them into a searchable repository, and to leverage the repository to propose to the users data transformations suitable for their needs. We study effectiveness and efficiency in an extensive evaluation over both real-world and synthetic transformation scenarios, and show that our system can infer, store, and search millions of meta-mappings in seconds.