SQL query recommendation suggests an SQL statement to a user, based on his submitted requests and on queries of other users stored in a log. Such methods need to be scalable and data-aware. Data awareness means that the filtering condition, the most crucial element of the recommendation, contains actual values. Otherwise, the query is not directly executable. Existing approaches do not satisfy the above requirements or are limited regarding the query types supported. We in turn propose DASQR, a data-aware and scalable SQL query recommender, which also outperforms competitors regarding quality and runtimes. For the evaluation, existing approaches have proposed adaptations of metrics such as precision or recall for the SQL domain, but then only use their measures. Our comparison is broader, including new adaptations of those measures and also several existing ones.

SQL查询建议根据用户提交的请求以及存储在日志中的其他用户的查询向用户建议一条SQL语句。这样的方法需要是可扩展的和数据感知的。数据意识意味着筛选条件（建议中最关键的元素）包含实际值。否则，查询不能直接执行。现有方法不能满足上述要求，或者在支持的查询类型方面受到限制。反过来，我们提出DASQR，这是一种数据感知和可扩展的SQL查询推荐器，在质量和运行时方面也优于竞争对手。为了进行评估，现有方法提出了对诸如SQL域的精度或召回率之类的指标进行调整的方法，但随后只能使用其度量。我们的比较范围更广，