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Foundations of Data Quality Management
Data quality is one of the most important problems in data management. A database system typically aims to support the creation, maintenance, and use of large amount of data, focusing on the quantity of data. However, real-life data are often dirty: inconsistent, duplicated, inaccurate, incomplete, or stale. Dirty data in a database routinely generate misleading or biased analytical results and decisions, and lead to loss of revenues, credibility and customers. With this comes the need for data quality management. In contrast to traditional data management tasks, data quality management enables the detection and correction of errors in the data, syntactic or semantic, in order to improve the...
Current Trends in Database Technology - EDBT 2006
This book constitutes the thoroughly refereed joint post-proceedings of nine workshops held as part of the 10th International Conference on Extending Database Technology, EDBT 2006, held in Munich, Germany in March 2006. The 70 revised full papers presented were selected from numerous submissions during two rounds of reviewing and revision.
Advances in Spatial and Temporal Databases
The refereed proceedings of the 8th International Symposium on Spatial and Temporal Databases, SSTD 2003, held at Santorini Island, Greece in July 2003. The 28 revised full papers presented together with a keynote paper were carefully reviewed and selected from 105 submissions. The papers are organized in topical sections on access methods, advanced query processing, data mining and data warehousing, distance-based queries, mobility and moving points management, modeling and languages, similarity processing, systems and implementation issues.
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