Data quality (DQ) assessment can be significantly enhanced with the use of the right DQ assessment methods, which provide automated solutions to assess DQ. The range of DQ assessment methods is very broad: from data profiling and semantic profiling to data matching and data validation. This paper gives an overview of current methods for DQ assessment and classifies the DQ assessment methods into an existing taxonomy of DQ problems. Specific examples of the placement of each DQ method in the taxonomy are provided and illustrate why the method is relevant to the particular taxonomy position. The gaps in the taxonomy, where no current DQ methods exist, show where new methods are required and can guide future research and DQ tool development
Large and over the years grown databases are a persistent concern in the field of data quality. Data...
International audienceOne challenging aspects of data quality modeling and management is to provide ...
International audienceOne challenging aspects of data quality modeling and management is to provide ...
Data quality (DQ) assessment and improvement in larger information systems would often not be feasib...
The literature provides a wide range of techniques to assess and improve the quality of data. Due to...
Abstract Background Data quality assessment is important but complex and task dependent. Identifying...
Data quality (DQ) has been studied in significant depth over the last two decades and has received a...
Data quality (DQ) in maintenance has become an increasinglyimportant aspect to many firms as most of...
Data quality (DQ) in maintenance has become an increasinglyimportant aspect to many firms as most of...
Data quality (DQ) in maintenance has become an increasinglyimportant aspect to many firms as most of...
Data quality (DQ) in maintenance has become an increasinglyimportant aspect to many firms as most of...
Industrial enterprises rely on prediction of market behavior, monitoring of performance measures, ev...
The assessment of data quality and suitability plays an important role in improving the validity and...
Data quality is a problem studied in many different research disciplines like computer science, stat...
The paper proposes a new data object-driven approach to data quality evaluation. It consists of thre...
Large and over the years grown databases are a persistent concern in the field of data quality. Data...
International audienceOne challenging aspects of data quality modeling and management is to provide ...
International audienceOne challenging aspects of data quality modeling and management is to provide ...
Data quality (DQ) assessment and improvement in larger information systems would often not be feasib...
The literature provides a wide range of techniques to assess and improve the quality of data. Due to...
Abstract Background Data quality assessment is important but complex and task dependent. Identifying...
Data quality (DQ) has been studied in significant depth over the last two decades and has received a...
Data quality (DQ) in maintenance has become an increasinglyimportant aspect to many firms as most of...
Data quality (DQ) in maintenance has become an increasinglyimportant aspect to many firms as most of...
Data quality (DQ) in maintenance has become an increasinglyimportant aspect to many firms as most of...
Data quality (DQ) in maintenance has become an increasinglyimportant aspect to many firms as most of...
Industrial enterprises rely on prediction of market behavior, monitoring of performance measures, ev...
The assessment of data quality and suitability plays an important role in improving the validity and...
Data quality is a problem studied in many different research disciplines like computer science, stat...
The paper proposes a new data object-driven approach to data quality evaluation. It consists of thre...
Large and over the years grown databases are a persistent concern in the field of data quality. Data...
International audienceOne challenging aspects of data quality modeling and management is to provide ...
International audienceOne challenging aspects of data quality modeling and management is to provide ...