Titlebook: Handbook of Data Quality; Research and Practic Shazia Sadiq Book 2013 Springer-Verlag Berlin Heidelberg 2013 data governance.data integrati

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Wojciech T. Sołowski,Scott W. Sloanuses (as compared to transactional databases) include data integration from multiple sources and emphasis on temporal, historical, and multidimensional data. In this chapter, we survey data warehouse quality problems and solutions, including data freshness (ensuring that materialized views are up to

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https://doi.org/10.1057/9780230249158ase in the number and complexity of data quality problems. Data glitches, a common name for data quality problems, can be simple and stand alone, or highly complex with spatial and temporal correlations. In this chapter, we provide an overview of a comprehensive and measurable data quality process.

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Detailliertes Screening im 1. Trimester been applicable to specific problems and domains. In the last few years we have seen the emergence of more generic solutions, and also of declarative and rule-based specifications of the intended solutions of data cleaning processes. In this chapter we review some of those recent developments.

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https://doi.org/10.1007/978-3-662-67635-6ese applications, . can be used to store uncertain data, and querying facilities are provided to yield answers with statistical confidence. Given that a limited amount of resources is available to “clean” the database (e.g., by probing some sensor data values to get their latest values), we address