AJAX: an extensible data cleaning tool
SIGMOD '00 Proceedings of the 2000 ACM SIGMOD international conference on Management of data
Proceedings of the twenty-first ACM SIGMOD-SIGACT-SIGART symposium on Principles of database systems
Common Warehouse Metamodel Developer's Guide
Common Warehouse Metamodel Developer's Guide
Data Mining and Knowledge Discovery
Data Quality: The Accuracy Dimension
Data Quality: The Accuracy Dimension
Summarizability in OLAP and Statistical Data Bases
SSDBM '97 Proceedings of the Ninth International Conference on Scientific and Statistical Database Management
Generating data quality rules and integration into ETL process
Proceedings of the ACM twelfth international workshop on Data warehousing and OLAP
Using inheritance in a metadata based approach to data quality assessment
Proceedings of the first international workshop on Model driven service engineering and data quality and security
The Effects and Interactions of Data Quality and Problem Complexity on Classification
Journal of Data and Information Quality (JDIQ)
An extensible metadata framework for data quality assessment of composite structures
DaWaK'07 Proceedings of the 9th international conference on Data Warehousing and Knowledge Discovery
| Hi-index | 0.00 |
The importance of metadata has been broadly referred in the last years, mainly in the field of data warehousing and decision support systems. Contemporarily, in the adjacent field of data quality, several approaches and tools have been set out for the purpose of data profiling and cleaning. However, little effort has been made in order to formally specify metrics and techniques for data quality in a structured way. As a matter of fact, little relevance has been assigned to metadata regarding data quality and data cleaning issues. This paper aims at filling this gap, proposing a conceptual metamodel for data quality and cleaning, both applicable to operational and data warehousing contexts. The presented metadata model is integrated with OMG's CWM, offering a possible extension of this standard toward data quality.