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It's a little annoying that the anonymised variable names sometimes but not always correspond to the table/column name they come from. E.g in some datasets like academic, the variable name is derived from the column name:
"sql": [
"SELECT JOURNALalias0.HOMEPAGE FROM JOURNAL AS JOURNALalias0 WHERE JOURNALalias0.NAME = \"journal_name0\" ;"
],
"variables": [
{
"example": "PVLDB",
"location": "both",
"name": "journal_name0",
"type": "journal_name"
}
]
whereas in geography, variables are named var1, from which you cannot directly infer their type from either the name or the type key.
"sql": [
"SELECT CITYalias0.CITY_NAME FROM CITY AS CITYalias0 WHERE CITYalias0.POPULATION = ( SELECT MAX( CITYalias1.POPULATION ) FROM CITY AS CITYalias1 WHERE CITYalias1.STATE_NAME = \"var0\" ) AND CITYalias0.STATE_NAME = \"var0\" ;"
],
"variables": [
{
"example": "arizona",
"location": "both",
"name": "var0",
"type": "state"
}
]
The text was updated successfully, but these errors were encountered:
Hmm yeah I also found this once I dug into it more - e.g the limit0 variables in the scholar dataset are really a function of the query rather than particular to the database. What you've done for geography looks like an improvement though!
It's a little annoying that the anonymised variable names sometimes but not always correspond to the table/column name they come from. E.g in some datasets like academic, the variable name is derived from the column name:
whereas in
geography
, variables are namedvar1
, from which you cannot directly infer their type from either the name or thetype
key.The text was updated successfully, but these errors were encountered: