representing the query, as described later. Unlike MSCN, in our model all the vectors of set $V$ have the same dimension and the same segmentation as depicted in Table \[table:Vector Segmentation\], where $\#T$ is the number of all the tables in the database, $\#C$ is the number of all the columns in all the database tables, and $\#O$ is the number of possible predicates operators. In total, the vector dimension is $\#T + 3*\#C + \#O + 1$, denoted as $L$.

The queries tables, joins and column predicates (sets $T$, $J$ and $P$) are inseparable, hence, treating each set individually using different neural networks may disorientate the model. Therefore, we choose to featurize these sets using the same vector format in order to ease learning.

Element of sets $T$, $J$, and $P$, are represented by vectors as follows (see a simple example in Figure \[example\]). All the vectors have the same dimension $L$. Each table $t \in T$ is represented by a unique one-hot vector (a binary vector of length $\#T$ with a single non-zero entry, uniquely identifying a specific table) placed in the T-seg segment. Each join clause of the form $(col1,=,col2) \in J$ is represented as follows. $col1$ 