Data cleaning

著者

    • Ilyas, Ihab F.
    • Xu, Chu

書誌事項

Data cleaning

Ihab F. Ilyas, Xu Chu

(ACM books, #28)

Association for Computing Machinery, c2019

  • : hardcover

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注記

Includes bibliographical references (p. [227]-246) and index

内容説明・目次

内容説明

This is an overview of the end-to-end data cleaning process. Data quality is one of the most important problems in data management, since dirty data often leads to inaccurate data analytics results and incorrect business decisions. Poor data across businesses and the U.S. government are reported to cost trillions of dollars a year. Multiple surveys show that dirty data is the most common barrier faced by data scientists. Not surprisingly, developing effective and efficient data cleaning solutions is challenging and is rife with deep theoretical and engineering problems. This book is about data cleaning, which is used to refer to all kinds of tasks and activities to detect and repair errors in the data. Rather than focus on a particular data cleaning task, this book describes various error detection and repair methods, and attempts to anchor these proposals with multiple taxonomies and views. Specifically, it covers four of the most common and important data cleaning tasks, namely, outlier detection, data transformation, error repair (including imputing missing values), and data deduplication. Furthermore, due to the increasing popularity and applicability of machine learning techniques, it includes a chapter that specifically explores how machine learning techniques are used for data cleaning, and how data cleaning is used to improve machine learning models. This book is intended to serve as a useful reference for researchers and practitioners who are interested in the area of data quality and data cleaning. It can also be used as a textbook for a graduate course. Although we aim at covering state-of-the-art algorithms and techniques, we recognize that data cleaning is still an active field of research and therefore provide future directions of research whenever appropriate.

目次

Preface Figure and Table Credits Introduction Outlier Detection Data Deduplication Data Transformation Data Quality Rule Definition and Discovery Rule-Based Data Cleaning Machine Learning and Probabilistic Data Cleaning Conclusion and Future Thoughts References Index Author Biographies

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関連文献: 1件中  1-1を表示

  • ACM books

    Morgan & Claypool Publishers

    所蔵館1館

詳細情報

  • NII書誌ID(NCID)
    BC09707596
  • ISBN
    • 9781450371520
  • 出版国コード
    us
  • タイトル言語コード
    eng
  • 本文言語コード
    eng
  • 出版地
    [New York, N.Y.]
  • ページ数/冊数
    xix, 260 p.
  • 大きさ
    25 cm
  • 分類
  • 親書誌ID
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