Cleaning Denial Constraint Violations through Relaxation
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Data cleaning is a time-consuming process which depends on the data analysis that users perform. Existing solutions treat data cleaning as a separate, offline process, that takes place before analysis starts. Applying data cleaning before analysis assumes a priori knowledge of the inconsistencies and the query workload, thereby requiring effort on understanding and cleaning data unnecessary for the analysis. This paper proposes an approach that performs probabilistic repairing of denial constraint violations on-demand, driven by the exploratory analysis that users perform. It introduces Daisy, a system that integrates data cleaning seamlessly into the analysis by relaxing query results. Daisy executes analytical query workloads over dirty data by weaving cleaning operators into the query plan. Our evaluation shows that Daisy adapts to the workload, and outperforms traditional offline cleaning on both synthetic and real-world workloads.
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