start problem”). Next, we describe how we address each one of these questions.

Cold Start Problem {#Defining the database and the development set}
------------------

### Defining the Database {#Defining the database}

We generated a training-set, and later on evaluated our model on it, using the IMDb database. IMDb contains many correlations and has been shown to be very challenging for cardinality estimators [@HowGoodCar]. This database contains a plethora of information about movies and related facts about actors, directors, and production companies, with more than 2.5M movie titles produced over 130 years (starting from 1880) by 235,000 different companies with over 4M actors.

### Generating the Development Dataset {#queries generator}

Our approach for solving the “cold start problem” is to obtain an initial training corpus using a specialized queries generator that randomly generates queries based on the IMDB schema and the actual columns values. Our queries generator generates the dataset in three main steps. In the first step, it repeatedly generates multiple SQL queries as follows. It randomly chooses a set of tables $t$, that can join with each other in the database. Then, it adds the corresponding join edges to the query. For each base table $bt$ in the chosen 