Data imputation is a well-known technique for repairing missing data values but can incur a prohibitive cost when applied to large data sets. Query-driven imputation offers a better alternative as it allows for fixing only the data that is relevant for a query. We adopt a rule-based query rewriting technique for imputing the answers of analytic queries that are missing or suffer from incorrectness due to data incompleteness. We present a novel query rewriting mechanism that is guided by partition patterns which are compact representations of complete and missing data partitions. Our solution strives to infer the largest possible set of missing answers while improving the precision of incorrect ones.