Problems , Methods , and Challenges in Comprehensive Data Cleansing
Problems , Methods , and Challenges in Comprehensive Data Cleansing
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发表时间：
2005
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通讯作者：
H. Mueller;J. Freytag
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作者：
H. Mueller;J. Freytag
清除数据中的杂质是数据处理和维护的一个组成部分。这导致了一系列旨在提高准确性的方法的发展，从而提高现有数据的可用性。本文介绍了数据清洗的问题，途径和方法的调查。我们对数据中发生的各种类型的异常进行分类，这些异常必须被消除，我们定义了一组全面清理数据必须实现的质量标准。基于这种分类，我们评估和比较现有的数据清洗方法处理和消除异常的类型。我们还描述了一般的数据清洗的不同步骤，并指定清洗过程中使用的方法，并给出了一个展望的研究方向，补充现有的系统。
Cleansing data from impurities is an integral part of data processing and maintenance. This has lead to the development of a broad range of methods intending to enhance the accuracy and thereby the usability of existing data. This paper presents a survey of data cleansing problems, approaches, and methods. We classify the various types of anomalies occurring in data that have to be eliminated, and we define a set of quality criteria that comprehensively cleansed data has to accomplish. Based on this classification we evaluate and compare existing approaches for data cleansing with respect to the types of anomalies handled and eliminated by them. We also describe in general the different steps in data clean-sing and specify the methods used within the cleansing process and give an out-look to research directions that complement the existing systems.