期刊
出版社
ASSOC COMPUTING MACHINERY
DOI: 10.1145/3318464.3389775
关键词
data cleaning; denial constraints; adaptive cleaning
资金
- EU FP7 programme ERC-2013-CoG [617508]
- European Union's Horizon 2020 research and innovation programme [825041]
Data cleaning is a time-consuming process that depends on the data analysis that users perform. Existing solutions treat data cleaning as a separate offline process that takes place before analysis begins. Applying data cleaning before analysis assumes a priori knowledge of the inconsistencies and the query workload, thereby requiring effort on understanding and cleaning the data that is unnecessary for the analysis. We propose an approach that performs probabilistic repair of denial constraint violations on-demand, driven by the exploratory analysis that users perform. We introduce Daisy, a system that seamlessly integrates data cleaning 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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