Consistent query answers in inconsistent databases
PODS '99 Proceedings of the eighteenth ACM SIGMOD-SIGACT-SIGART symposium on Principles of database systems
AJAX: an extensible data cleaning tool
SIGMOD '00 Proceedings of the 2000 ACM SIGMOD international conference on Management of data
ACM Computing Surveys (CSUR)
Declarative Data Cleaning: Language, Model, and Algorithms
Proceedings of the 27th International Conference on Very Large Data Bases
Potter's Wheel: An Interactive Data Cleaning System
Proceedings of the 27th International Conference on Very Large Data Bases
Relational lenses: a language for updatable views
Proceedings of the twenty-fifth ACM SIGMOD-SIGACT-SIGART symposium on Principles of database systems
One-to-many data transformations through data mappers
Data & Knowledge Engineering
Improving data quality: consistency and accuracy
VLDB '07 Proceedings of the 33rd international conference on Very large data bases
Database Systems: The Complete Book
Database Systems: The Complete Book
On co-authorship for author disambiguation
Information Processing and Management: an International Journal
Efficiently incorporating user feedback into information extraction and integration programs
Proceedings of the 2009 ACM SIGMOD International Conference on Management of data
Conditional Dependencies: A Principled Approach to Improving Data Quality
BNCOD 26 Proceedings of the 26th British National Conference on Databases: Dataspace: The Final Frontier
Generating data quality rules and integration into ETL process
Proceedings of the ACM twelfth international workshop on Data warehousing and OLAP
A generic and customizable framework for the design of ETL scenarios
Information Systems - Special issue: The 15th international conference on advanced information systems engineering (CAiSE 2003)
Proceedings of the VLDB Endowment
Graph-based modeling of ETL activities with multi-level transformations and updates
DaWaK'05 Proceedings of the 7th international conference on Data Warehousing and Knowledge Discovery
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Data cleaning and ETL processes are usually modeled as graphs of data transformations. The involvement of the users responsible for executing these graphs over real data is important to tune data transformations and to manually correct data items that cannot be treated automatically. In this paper, in order to better support the user involvement in data cleaning processes, we equip a data cleaning graph with data quality constraints to help users identifying the points of the graph and the records that need their attention and manual data repairs for representing the way users can provide the feedback required to manually clean some data items. We provide preliminary experimental results that show the significant gains obtained with the use of data cleaning graphs.