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Nowadays, machine learning plays a key role in developing plenty ofapplications, e.g., smart homes, smart medical assistance, and autonomousdriving. A major challenge of these applications is preserving high quality ofthe training and the serving data. Nevertheless, existing data cleaning methodscannot exploit context information. Thus, they usually fail to track shifts inthe data distributions or the associated error profiles. To overcome theselimitations, we introduce, in this paper, a novel method for automated tabulardata cleaning powered by dynamic functional dependency rules extracted from alive context model. As a proof of concept, we create a smart home use case tocollect data while preserving the context information. Using two different datasets, our evaluations show that the proposed cleaning method outperforms a setof baseline methods in terms of the detection and repair accuracy.

RTClean: Context-aware Tabular Data Cleaning using Real-time OFDs