Workshop Organizers’ Message
Abstract
Poor data quality is known to compromise the credibility and efficiency of commercial as well as public endeavours. Several developments from industry and academia have contributed significantly towards addressing the problem. These typically include analysts and practitioners who have contributed to the design of strategies and methodologies for data governance; solution architects including software vendors who have contributed towards appropriate system architectures that promote data integration and; and data experts who have contributed to data quality problems such as duplicate detection, identification of outliers, consistency checking and many more through the use of computational techniques. The attainment of true data quality lies at the convergence of the three aspects, namely organizational, architectural and computational. Fulltext Preview
Keywords
poor data quality, credibility, efficiency, system architectures, duplicate detection, identifiction outliers
Date of this Version
2009
Comments
Database Systems for Advanced Applications Lecture Notes in Computer Science, 2009, Volume 5667/2009, 95-96,