Data Quality and its Staleness dimension
Understanding, measuring and controlling
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By its nature, term "data quality" with its generic meaning "fitness for use" has both subjective and objective aspects. To demonstrate how one can benefit from measuring and controlling quality of one's data, in this book we presented three real world use cases which demonstrate a top-down research approach of the data quality scope in three different real world applications. In particular, we study the following problems: 1) how quality of data can be defined and propagated to customers in a business intelligence application for quality-aware decision making; 2) how data quality can be defin...
By its nature, term "data quality" with its generic meaning "fitness for use" has both subjective and objective aspects. To demonstrate how one can benefit from measuring and controlling quality of one's data, in this book we presented three real world use cases which demonstrate a top-down research approach of the data quality scope in three different real world applications. In particular, we study the following problems: 1) how quality of data can be defined and propagated to customers in a business intelligence application for quality-aware decision making; 2) how data quality can be defined, measured and used in a web-based system operating with semi-structured data from and designated to both humans and machines; 3) how a data-driven (vs. system-driven) time-related data quality notion of staleness can be defined, efficiently measured and monitored in a generic information system. The work should help researchers and professionals working on both generic data quality problems as its understanding in a given context, and on data quality's specific applications as measurement its dimensions.
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