## IIT Database Group

### Abstract

Though partially automated, developing schema mappings remains a complex and potentially error-prone task. In this paper, we present TRAMP (TRAnsformation Mapping Provenance), an extensive suite of tools supporting the debugging and tracing of schema mappings and transformation queries. TRAMP combines and extends data provenance with two novel notions, transformation provenance and mapping provenance, to explain the relationship between transformed data and those transformations and mappings that produced that data. In addition we provide query support for transformations, data, and all forms of provenance. We formally define transformation and mapping provenance, present an efficient implementation of both forms of provenance, and evaluate the resulting system through extensive experiments.

### bibtex

@article{GA10,
author = {Glavic, Boris and Alonso, Gustavo and Miller, Ren\'{e}e J. and Haas, Laura M.},
date-modified = {2012-12-18 17:17:43 +0000},
journal = {Proceedings of the Very Large Data Bases Endowment},
keywords = {TRAMP; Provenance; Data Exchange},
number = {1},
pages = {1314-1325},
pdfurl = {http://cs.iit.edu/%7edbgroup/assets/pdfpubls/GA10.pdf},
projects = {TRAMP},
slideurl = {http://www.slideshare.net/lordPretzel/2010-vldb-tramp},
title = {{TRAMP: Understanding the Behavior of Schema Mappings through Provenance}},
venueshort = {PVLDB},
volume = {3},
year = {2010},
bdsk-url-1 = {http://cs.iit.edu/%7edbgroup/assets/pdfpubls/GA10.pdf}
}


### Reference

TRAMP: Understanding the Behavior of Schema Mappings through Provenance Boris Glavic, Gustavo Alonso, Renée J. Miller and Laura M. Haas Proceedings of the Very Large Data Bases Endowment. 3, 1 (2010) , 1314–1325.