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 integrating heterogeneous data sources and should be addressed together with schema-related data transformations. In data

of universal keys across dierent databases that is known as the object identity problem, (2) the existence of keyboard errors in the data, and (3) the presence of inconsistencies in data coming from multiple sources. Dealing with these problems is globally called the data cleaning process. In this work, we

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 integrating heterogeneous data sources and should be addressed together with schema-related data transformations. In data

Data Cleaning methods are used for finding duplicates within a file or across sets of files. This overview provides background on the Fellegi-Sunter model of record linkage. The Fellegi-Sunter model provides an optimal theoretical classification rule. Fellegi and Sunter introduced methods

Addressed in this paper is the issue of ‘email data cleaning ’ for text mining. Many text mining applications need take emails as input. Email data is usually noisy and thus it is necessary to clean it before mining. Several products offer email cleaning features, however, the types of noises

Cleaning organizational data of discrepancies in structure and content is important for data warehousing and Enterprise Data Integration (EDI). Current commercial solutions for data cleaning involve many iterations of time-consuming "data quality" analysis to find errors, and long

Any number you want: the impact of data cleaning on internal validity

The problem of data cleaning, which consists of removing inconsistencies and errors from original data sets, is well known in the area of decision support systems and data warehouses. This holds regardless of the application - relational database joining, web-related, or scientific. In all cases

Abstract—In declarative data cleaning, data semantics are encoded as constraints and errors arise when the data violates the constraints. Various forms of statistical and logical inference can be used to reason about and repair inconsistencies (errors) in data. Recently, unified approaches

Data-cleaning (or data-repairing) is considered a crucial problem in many database-related tasks. It consists in making a database consistent with respect to a set of given constraints. In recent years, repairing methods have been proposed for several classes of constraints. However, these methods