研究了基于符号语义的不完整数据聚集查询处理问题.不完整数据又称为缺失数据，缺失值包括可填充的和不可填充的两种类型.现有的缺失值填充算法不能保证填充后查询结果的准确度，为此，给出了不完整数据聚集查询结果的区间估计.在符号语义中扩展了传统关系数据库模型，提出了一种通用不完整数据库模型.该模型可以处理可填充的和不可填充的两种类型缺失值.在该模型下，提出一种新的不完整数据聚集查询结果语义：可靠结果.可靠结果是真实查询结果的区间估计，可以保证真实查询结果有很大概率在该估计区间范围内.给出了线性时间求解SUM、COUNT和AVG查询可靠结果的方法.真实数据集和合成数据集上的扩展实验验证了所提方法的有效性.

This work studies the problem of aggregate query processing over incomplete data based on denotational semantics. Incomplete data is also known as missing values and can be classified into two categories:applicable nulls and inapplicable nulls. Existing imputation algorithms cannot guarantee the accuracy of the query result after imputation. The interval estimation of the aggregate query result is given. This study extends the relational model under the denotational semantic, which can cover all types of incomplete data. A new semantic of aggregate query answers over incomplete data is defined. Reliable answers are interval estimations of the ground-truth query results, which can cover the ground-truth results with high probability. For SUM, COUNT, and AVG queries, linear approximate evaluation algorithms are proposed to compute reliable answers. The extended experiments on the real datasets and synthetic datasets verify the effectiveness of the method proposed in this study.

TP311

国家自然科学基金（61702344）