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Written for practitioners of data mining, data cleaning and database management.
Presents a technical treatment of data quality including process, metrics, tools and algorithms.
Focuses on developing an evolving modeling strategy through an iterative data exploration loop and incorporation of domain knowledge.
Addresses methods of detecting, quantifying and correcting data quality issues that can have a significant impact on findings and decisions, using commercially available tools as well as new algorithmic approaches.
Uses case studies to illustrate applications in real life scenarios.
Highlights new approaches and methodologies, such as the DataSphere space partitioning and summary based analysis techniques.
Author Biography
TAMRAPARNI DASU, PhD, and THEODORE JOHNSON, PhD, are both members of the technical staff at AT&T Labs-Research in Florham Park, New Jersey.
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