We classify data quality problems that are addressed by data cleaning and provide an
overview of the main solution approaches. Data cleaning is especially required when …

Background The life sciences, biomedicine and health care are increasingly turning into a
data intensive science [2–4]. Particularly in bioinformatics and computational biology we …

Deduplication is a key operation in integrating data from multiple sources. The main
challenge in this task is designing a function that can resolve when a pair of records refer to …

Cleaning data of errors in structure and content is important for data warehousing and
integration. Current solutions for data cleaning involve many iterations of data “auditing” to …

8 Completeness-Driven Query Optimization Page 1 8 Completeness-Driven Query
Optimization Completeness measures the usefulness of a source or of a plan to answer a …

When integrating information from multiple websites, the same data objects can exist in
inconsistent text formats across sites, making it difficult to identify matching objects using …

Data mining research typically assumes that the data to be analyzed has been identified,
gathered, cleaned, and processed into a convenient form. While data mining tools greatly …

Sensors in smart-item environments capture data about product conditions and usage to
support business decisions as well as production automation processes. A challenging …

Existing data cleaning methods work on the basis of computing the degree of similarity
between nearby records in a sorted database. High recall is achieved by accepting records …

对数据质量, 尤其是数据清洗的研究进行了综述. 首先说明数据质量的重要性和衡量指标,
定义了数据清洗问题. 然后对数据清洗问题进行分类, 并分析了解决这些问题的途径 …