A first course in database systems
A first course in database systems
Business Rules and Information Systems: Aligning It with Business Goals
Business Rules and Information Systems: Aligning It with Business Goals
Information and Database Quality
Information and Database Quality
Principles of the Business Rule Approach
Principles of the Business Rule Approach
Data Quality: The Accuracy Dimension
Data Quality: The Accuracy Dimension
Potter's Wheel: An Interactive Data Cleaning System
Proceedings of the 27th International Conference on Very Large Data Bases
Exploratory Data Mining and Data Cleaning
Exploratory Data Mining and Data Cleaning
A data quality metamodel extension to CWM
APCCM '07 Proceedings of the fourth Asia-Pacific conference on Comceptual modelling - Volume 67
Checks and balances: monitoring data quality problems in network traffic databases
VLDB '03 Proceedings of the 29th international conference on Very large data bases - Volume 29
Systematic development of data mining-based data quality tools
VLDB '03 Proceedings of the 29th international conference on Very large data bases - Volume 29
Proceedings of the sixteenth ACM conference on Conference on information and knowledge management
Referential integrity quality metrics
Decision Support Systems
Data Quality and Record Linkage Techniques
Data Quality and Record Linkage Techniques
Progressive Methods in Data Warehousing and Business Intelligence: Concepts and Competitive Analytics
Support for user involvement in data cleaning
DaWaK'11 Proceedings of the 13th international conference on Data warehousing and knowledge discovery
E-ETL: framework for managing evolving etl processes
Proceedings of the 4th workshop on Workshop for Ph.D. students in information & knowledge management
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Many data quality projects are integrated into data warehouse projects without enough time allocated for the data quality part, which leads to a need for a quicker data quality process implementation that can be easily adopted as the first stage of data warehouse implementation. We will see that many data quality rules can be implemented in a similar way, and thus generated based on metadata tables that store information about the rules. These generated rules are then used to check data in designated tables and mark erroneous records, or to do certain updates of invalid data. We will also store information about the rules violations in order to provide analysis of such data. This could give a significant insight into our source systems. Entire data quality process will be integrated into ETL process in order to achieve load of data warehouse that is as automated, as correct and as quick as possible. Only small number of records would be left for manual inspection and reprocessing.