关键词: 缺失值填充, 聚集查询, 块采样

Abstract: Recently, both industrial and academic worlds suffer from the problem of incomplete data. Incomplete data（missing value） significantly reduces the value of data. Existing missing data imputation techniques with high time complexity hardly meet the requirements of real-time applications in the big data era. This paper focuses on how to efficiently evaluate aggregation queries on incomplete data. Specifically, missing data imputation techniques are integrated with the sample-based approximate query processing. Besides, a block-level sampling strategy is adoptd to speed up the query processing. All missing values are imputed in the sample and an unbiased estimator of the truth aggregate result is derived. Experiments on both real dataset and synthetic dataset show that the method can produce significant improvements in speed while providing good quality answer.

Key words: incomplete data, aggregate query, block sampling