with MSCN {#Comparing with MSCN}

In order to make a fair comparison between the CRN model and the MSCN model, we train the MSCN model with the *same* data that was used to train the CRN model.

The CRN model takes two queries as input, whereas the MSCN model takes one query as input. Therefore, to address this issue, we created the training dataset for the MSCN model as follows. For each pair of queries $(Q1,Q2)$ used in training the CRN model, we added the following two input queries to the MSCN training set:

-   $Q1 {\mbox{$\cap$}}Q2$, along with its actual cardinality.

-   $Q1$, along with its actual cardinality.

Finally, we ensure that the training set includes only unique queries without repetition. This way, we both models, MSCN and CRN, are trained with the *same* information.

### Comparing with PostgreSQL

Comparing with PostgreSQL does not require generating an appropriate training set, since the PostgreSQL cardinality estimation component is based on database profiling techniques and does not require training.

Evaluation Workloads
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We evaluate CRN on the IMDb dataset as described in Section \[Defining the database\], using two different query workloads:

-   cnt\_test1, a synthetic workload generated by the same queries 