  The Group-By query is an important kind of query, which is common and widely
used in data warehouses, data analytics, and data visualization. Approximate
query processing is an effective way to increase the querying efficiency on big
data. The answer to a group-by query involves multiple values, which makes it
difficult to provide sufficiently accurate estimations for all the groups.
Stratified sampling improves the accuracy compared with the uniform sampling,
but the samples chosen for some special queries cannot work for other queries.
Online sampling chooses samples for the given query at query time, but it
requires a long latency. Thus, it is a challenge to achieve both accuracy and
efficiency at the same time. Facing such challenge, in this work, we propose a
sample generation framework based on a conditional generative model. The sample
generation framework can generate any number of samples for the given query
without accessing the data. The proposed framework based on the lightweight
model can be combined with stratified sampling and online aggregation to
improve the estimation accuracy for group-by queries. The experimental results
show that our proposed methods are both efficient and accurate.
