Abstract
For big data, data quality problem is more serious. Big data cleaning system requires scalability and the abilityof handling mixed errors. Motivated by this, we develop Cleanix, a prototype system for cleaning relational Big Data. Cleanix takes data integrated from multiple data sources and cleans them on a shared-nothing machine cluster. The backend system is built on-top-of an extensible and flexible data-parallel substrate the Hyracks framework. Cleanix supports various data cleaning tasks such as abnormal value detection and correction, incomplete data filling, de-duplication, and conflict resolution. In this paper, we show the organization, data cleaning algorithms as well as the design of Cleanix.
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