Cleaning Data With Selection Rules

Temporal Rules Discovery for Web Data Cleaning

Declarative rules, such as functional dependencies, are widely used for cleaning data. Several systems take them as input for detecting errors and computing a “clean” version of the data. To support domain experts,in specifying these rules, several tools have been proposed to profile the data and mine rules. However, existing discovery techniques have traditionally ignored the time dimension. R...

Discovering Editing Rules For Data Cleaning

Dirty data continues to be an important issue for companies. The database community pays a particular attention to this subject. A variety of integrity constraints like Conditional Functional Dependencies (CFD) have been studied for data cleaning. Data repair methods based on these constraints are strong to detect inconsistencies but are limited on how to correct data, worse they can even intro...

Editing Rules: Discovery and Application to Data Cleaning

Dirty data is a serious problem for businesses, leading to incorrect decision making, inefficient daily operations, and ultimately wasting both time and money. A variety of integrity constraints like Conditional Functional Dependencies (CFD) have been studied for data cleaning. Data repairing methods based on these constraints are strong to detect inconsistencies but are limited on how to corre...

Automatic Rules Generation Approach for Data Cleaning in Medical Applications

Research of Data Cleaning Methods Based on Dependency Rules

This paper introduces the concept and principle of data cleaning, analyzes the types and causes of dirty data, and proposes several key steps of typical cleaning process, puts forward a well scalability and versatility data cleaning framework, in view of data with attribute dependency relation, designs several of violation data discovery algorithms by formal formula, which can obtain inconsiste...