VLDB 2019, 45th International Conference on Very Large Data Bases, August 26-30, 2019, Los Angeles, California, USA
The de nition of mappings between heterogeneous schemas is a critical activity of any database application. Existing tools provide high level interfaces for the discovery of
correspondences between elements of schemas, but schema mappings need to be manually speci ed every time from scratch, even if the problem at hand is similar to one that has already been addressed. Schema mappings are precisely de ned over a pair of schemas and cannot directly be reused on different scenarios. We tackle this challenge
by generalizing schema mappings as meta-mappings: formalisms that describe transformations between generic data structures called meta-schemas. We formally characterize schema mapping reuse and explain how meta-mappings are suitable to capture enterprise knowledge from previously de fined schema mappings. We develop the techniques to infer meta-mappings from existing mappings, to organize them into a searchable repository, and to leverage the repository to propose to the users mappings suitable for their needs. We study effectiveness and efficiency in an extensive evalu-
ation over real-world scenarios, and show that our system can infer, store, and search millions of meta-mappings in seconds.
Type:
Conference
City:
Los Angeles
Date:
2019-08-26
Department:
Data Science
Eurecom Ref:
5633
Copyright:
© ACM, 2019. This is the author's version of the work. It is posted here by permission of ACM for your personal use. Not for redistribution. The definitive version was published in VLDB 2019, 45th International Conference on Very Large Data Bases, August 26-30, 2019, Los Angeles, California, USA http://dx.doi.org/10.14778/3303753.3303761
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