| 书目名称 | Data Quality | | 副标题 | Concepts, Methodolog | | 编辑 | Carlo Batini,Monica Scannapieca | | 视频video | | | 概述 | Details and analyzes different quality dimension definitions and parameters.Combines approaches from data modeling, data mining, knowledge representation, probability theory, statistical data analysis | | 丛书名称 | Data-Centric Systems and Applications | | 图书封面 |  | | 描述 | .Poor data quality can seriously hinder or damage the efficiency and effectiveness of organizations and businesses. The growing awareness of such repercussions has led to major public initiatives like the "Data Quality Act" in the USA and the "European 2003/98" directive of the European Parliament....Batini and Scannapieco present a comprehensive and systematic introduction to the wide set of issues related to data quality. They start with a detailed description of different data quality dimensions, like accuracy, completeness, and consistency, and their importance in different types of data, like federated data, web data, or time-dependent data, and in different data categories classified according to frequency of change, like stable, long-term, and frequently changing data. The book‘s extensive description of techniques and methodologies from core data quality research as well as from related fields like data mining, probability theory, statistical data analysis, and machine learning gives an excellent overview of the current state of the art. The presentation is completed by a short description and critical comparison of tools and practical methodologies, which will help readers | | 出版日期 | Book 2006 | | 关键词 | Data Accuracy; Data Availability; Data Completeness; Data Consistency; Data Integration; Data Quality; Dis | | 版次 | 1 | | doi | https://doi.org/10.1007/3-540-33173-5 | | isbn_softcover | 978-3-642-06970-3 | | isbn_ebook | 978-3-540-33173-5Series ISSN 2197-9723 Series E-ISSN 2197-974X | | issn_series | 2197-9723 | | copyright | Springer-Verlag Berlin Heidelberg 2006 |
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发表于 2025-3-21 17:23:42
