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Final Activity and Management Report Summary - EXQUALIBUR (Quality-Introspective Data Management System)

Data quality management has become one of the "hot topics" of emerging interest in various international academic and industrial communities involved in the management of data (Databases, Statistics, Workflow Management, Knowledge Engineering and Discovery from Databases). Multidisciplinary approaches are necessary to explore massive data sets, efficiently detect data quality problems (such as duplicates, errors, outliers, disguised missing values, misspellings, contradictions, inconsistencies, stale or incomplete data), correct errors, improve and ensure the quality of data in the databases.

The overall objective of EXQUALIBUR project (MOIF-CT-2006-041000) was to propose theoretically founded solutions for controlling the quality of data with methods combining statistics, data mining and database engineering. The most important achievements made by EXQUALIBUR are at the frontier between Database System Engineering and Statistics for 1) detecting data anomalies and anomaly patterns in massive datasets and 2) tracing the propagation of errors from one data source to another and discover data source dependence and relationships.

The methods proposed by EXQUALIBUR made an important step towards a new generation of quality-introspective data management systems, i.e., systems that are able to evaluate, control and improve automatically the quality of the data they store.

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