  Cardinality estimation is a fundamental task in database query processing and
optimization. As shown in recent papers, machine learning (ML)-based approaches
can deliver more accurate cardinality estimations than traditional approaches.
However, a lot of example queries have to be executed during the model training
phase to learn a data-dependent ML model leading to a very time-consuming
training phase. Many of those example queries use the same base data, have the
same query structure, and only differ in their predicates. Thus, index
structures appear to be an ideal optimization technique at first glance.
However, their benefit is limited. To speed up this model training phase, our
core idea is to determine a predicate-independent pre-aggregation of the base
data and to execute the example queries over this pre-aggregated data. Based on
this idea, we present a specific aggregate-enabled training phase for ML-based
cardinality estimation approaches in this paper. As we are going to show with
different workloads in our evaluation, we are able to achieve an average
speedup of 63 with our aggregate-enabled training phase.
