Data freshness and data accuracy :a state of the art
Resumen:
In a context of Data Integration Systems (DIS) providing access to large amounts of data extracted and integrated from autonomous data sources, users are highly concerned about data quality. Traditionally, data quality is characterized via multiple quality factors. Among the quality dimensions that have been proposed in the literature, this report analyzes two main ones: data freshness and data accuracy. Concretely, we analyze the various definitions of both quality dimensions, their underlying metrics and the features of DIS that impact their evaluation. We present a taxonomy of existing works proposed for dealing with both quality dimensions in several kinds of DIS and we discuss open research problems.
| 2006 | |
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Data freshness Data accuracy Data quality evaluation Data integration systems |
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| Universidad de la República | |
| COLIBRI | |
| http://hdl.handle.net/20.500.12008/3532 | |
| Acceso abierto | |
| Licencia Creative Commons Atribución – No Comercial – Sin Derivadas (CC BY-NC-ND 4.0) |
| Sumario: | In a context of Data Integration Systems (DIS) providing access to large amounts of data extracted and integrated from autonomous data sources, users are highly concerned about data quality. Traditionally, data quality is characterized via multiple quality factors. Among the quality dimensions that have been proposed in the literature, this report analyzes two main ones: data freshness and data accuracy. Concretely, we analyze the various definitions of both quality dimensions, their underlying metrics and the features of DIS that impact their evaluation. We present a taxonomy of existing works proposed for dealing with both quality dimensions in several kinds of DIS and we discuss open research problems. |
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