Data and Information Quality: Dimensions, Principles and Techniques (Data-Centric Systems and Applications)
暫譯: 數據與資訊品質:維度、原則與技術(數據中心系統與應用)

Carlo Batini, Monica Scannapieco

  • 出版商: Springer
  • 出版日期: 2018-05-26
  • 售價: $5,760
  • 貴賓價: 9.5$5,472
  • 語言: 英文
  • 頁數: 500
  • 裝訂: Paperback
  • ISBN: 3319795813
  • ISBN-13: 9783319795812
  • 海外代購書籍(需單獨結帳)

商品描述

This book provides a systematic and comparative description of the vast number of research issues related to the quality of data and information. It does so by delivering a sound, integrated and comprehensive overview of the state of the art and future development of data and information quality in databases and information systems.

To this end, it presents an extensive description of the techniques that constitute the core of data and information quality research, including record linkage (also called object identification), data integration, error localization and correction, and examines the related techniques in a comprehensive and original methodological framework. Quality dimension definitions and adopted models are also analyzed in detail, and differences between the proposed solutions are highlighted and discussed. Furthermore, while systematically describing data and information quality as an autonomous research area, paradigms and influences deriving from other areas, such as probability theory, statistical data analysis, data mining, knowledge representation, and machine learning are also included. Last not least, the book also highlights very practical solutions, such as methodologies, benchmarks for the most effective techniques, case studies, and examples.

The book has been written primarily for researchers in the fields of databases and information management or in natural sciences who are interested in investigating properties of data and information that have an impact on the quality of experiments, processes and on real life. The material presented is also sufficiently self-contained for masters or PhD-level courses, and it covers all the fundamentals and topics without the need for other textbooks. Data and information system administrators and practitioners, who deal with systems exposed to data-quality issues and as a result need a systematization of the field and practical methods in the area, will also benefit from the combination of concrete practical approaches with sound theoretical formalisms.

商品描述(中文翻譯)

本書系統性且比較性地描述了與數據和信息質量相關的眾多研究議題。它通過提供一個健全、整合且全面的概述,來展示數據和信息質量在數據庫和信息系統中的最新技術狀態及未來發展。

為此,本書詳細描述了構成數據和信息質量研究核心的技術,包括記錄連結(也稱為對象識別)、數據整合、錯誤定位和修正,並在一個全面且原創的方法論框架中檢視相關技術。質量維度的定義和採用的模型也被詳細分析,並突顯和討論所提出解決方案之間的差異。此外,在系統性描述數據和信息質量作為一個自主研究領域的同時,還包括了來自其他領域的範式和影響,例如概率論、統計數據分析、數據挖掘、知識表示和機器學習。最後,本書還強調了非常實用的解決方案,如方法論、最有效技術的基準、案例研究和示例。

本書主要為數據庫和信息管理領域的研究人員或自然科學領域的研究者撰寫,旨在探討影響實驗、過程及現實生活質量的數據和信息特性。所呈現的材料也足夠自成體系,適合碩士或博士級課程,涵蓋所有基礎知識和主題,而無需其他教科書。處理數據質量問題的數據和信息系統管理員及實務工作者,將從具體實用方法與健全理論形式主義的結合中受益。

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