Data cleaning is, in fact, a lively subject that has played an important part in the history of data management and data analytics, and it still is undergoing rapid development. Moreover, data cleaning is considered as a main challenge in the era of big data, due to the increasing volume, velocity and variety of data in many applications. This paper aims to provide an overview of recent work in...

In this paper, we propose and study a type of tuple-level constraints that arises from the selection operator σ relational algebra closely resembles concepts denial constraints. We call selection rules their properties in setting data consistency management. The main contribution paper is rule ...

Data Cleaning is a long standing problem, which is growing in importance with the mass of uncurated web data. State of the art approaches for handling inconsistent data are systems that learn and use conditional functional dependencies (CFDs) to rectify data. These methods learn data patterns–CFDs–from a clean sample of the data and use them to rectify the dirty/inconsistent data. While getting...

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