Introduction to the special issue on data quality

The solicited papers on issues arising in detecting data anomalies as well as assessing, monitoring, improving, and maintaining the quality of information, which give an excellent overview of the most important topics in data quality research today.

Abstract

Poor data quality in databases, data warehouses, and information systems affects every application domain. Many data processing tasks, such as information integration, data sharing, information retrieval, information extraction, and knowledge discovery require various forms of data preparation and consolidation with complex data processing techniques. These tasks usually assume that the data input contains no missing, inconsistent or incorrect values. This leaves a large gap between the available “dirty” data and the machinery to effectively process the data for the application purposes. In addition, tasks such as data integration and information extraction may themselves introduce errors in the data.