Detecting and repairing dirty data is one of the perennial challenges in data analytics, and
failure to do so can result in inaccurate analytics and unreliable decisions. Over the past few …

Massive amounts of data are available for the organization which will influence their
business decision. Data collected from the various resources are dirty and this will affect the …

AI models are increasingly applied in high-stakes domains like health and conservation.
Data quality carries an elevated significance in high-stakes AI due to its heightened …

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 …

Data collection is a major bottleneck in machine learning and an active research topic in
multiple communities. There are largely two reasons data collection has recently become a …

Abstract Machine learning (ML) has become a vital part in many aspects of our daily life.
However, building well performing machine learning applications requires highly …

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 …

With the rise of big data, there has been an increasing need for practitioners in this space
and an increasing opportunity for researchers to understand their workflows and design new …

This textbook explores the different aspects of data mining from the fundamentals to the
complex data types and their applications, capturing the wide diversity of problem domains …

Correctly detecting the semantic type of data columns is crucial for data science tasks such
as automated data cleaning, schema matching, and data discovery. Existing data …