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SUMMARY:Yiming Lin: QUIP: Query-driven Missing Value Imputation
DESCRIPTION:QUIP: Query-driven Missing Value Imputation\n\n\n\n\n\n\n\nThis paper develops a query-time missing value imputation frame- work\, entitled QUIP\, that minimizes the joint costs of imputation and query execution. QUIP achieves this by modifying how rela- tional operators are processed. It adds a cost-based decision function in each operator that checks whether the operator should invoke imputation prior to execution or to defer the imputations for down- stream operators to resolve. QUIP implements a new approach to evaluating outer join that preserve missing values during query processing\, and a bloom filter based index structure to optimize the space and running overhead. We have implemented QUIP using ImputeDB – a specialized database engine for data cleaning. Exten- sive experiments on both real and synthetic data sets demonstrates the effectiveness and efficiency of QUIP\, which outperforms the state-of-the-art ImputeDB by 2 to 10 times on different query sets and data sets\, and achieves the order-of-magnitudes improvement over offline approach. \n\n\n\n\n\n\n\n\nBio:\n\nYiming is a final year PhD student working with Prof. Sharad Mehrotra. His research area focuses on data management\, and especially on efficient query processing\, query optimization\, data quality and data integration.
URL:https://isg.ics.uci.edu/event/yiming-lin-quip-query-driven-during-missing-value-imputation/
LOCATION:DBH 4011
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