Provenance management in curated databases
Proceedings of the 2006 ACM SIGMOD international conference on Management of data
Data integration: the teenage years
VLDB '06 Proceedings of the 32nd international conference on Very large data bases
Databases with uncertainty and lineage
The VLDB Journal — The International Journal on Very Large Data Bases
Provenance for Computational Tasks: A Survey
Computing in Science and Engineering
The ORCHESTRA Collaborative Data Sharing System
ACM SIGMOD Record
A framework for fine-grained data integration and curation, with provenance, in a dataspace
TAPP'09 First workshop on on Theory and practice of provenance
Provenance query evaluation: what's so special about it?
Proceedings of the 18th ACM conference on Information and knowledge management
Lineage tracing in mediator-based information integration systems
ISSADS'05 Proceedings of the 5th international conference on Advanced Distributed Systems
| Hi-index | 0.00 |
In some integration applications, users are allowed to import data from heterogeneous sources, but are not allowed to update source data directly. Imported data may be inconsistent, and even when inconsistencies are detected and solved, these changes may not be propagated to the sources due to their update policies. Therefore, they continue to provide the same inconsistent data in the future until the proper authority updates them. In this paper, we propose PrInt, a model that supports user's decisions on cleaning data to be automatically reapplied in subsequent integration processes. By reproducing previous decisions, the user may focus only on new inconsistencies originated from source modified data. The reproducibility provided by PrInt is based on logging, and by incorporating data provenance in the integration process.