Task: equi-join-detect (source dataset: Auto-BI)

Your are given a database with related tables used for business intelligence analysis. Your task is to identify all key/foreign-key join relationships between the tables for this database. Recall that a join is a relationship between two tables, that allows rows from one table to be combined with rows from another, based on a set of common join columns.
 
 
No explanation needed. Please return all join relationships in the following JSON format:
{
  "joins": [
    {
      "from_table": "table-1",
      "from_column": "column-1",
      "to_table": "table-2",
      "to_column": "column-3"
    }, 
	...
  ]
}

Tables:

Table: Date
|   Year | Quarter   |   MonthNo | MonthName   |   MonthID | Date                    |
|-------:|:----------|----------:|:------------|----------:|:------------------------|
|   1999 | Q3        |         7 | Jul         |    199907 | 1999-07-01 00:00:00.000 |
|   1999 | Q3        |         7 | Jul         |    199907 | 1999-07-02 00:00:00.000 |
|   1999 | Q3        |         7 | Jul         |    199907 | 1999-07-03 00:00:00.000 |
|   1999 | Q3        |         7 | Jul         |    199907 | 1999-07-04 00:00:00.000 |
|   1999 | Q3        |         7 | Jul         |    199907 | 1999-07-05 00:00:00.000 |
|   1999 | Q3        |         7 | Jul         |    199907 | 1999-07-06 00:00:00.000 |
|   1999 | Q3        |         7 | Jul         |    199907 | 1999-07-07 00:00:00.000 |
|   1999 | Q3        |         7 | Jul         |    199907 | 1999-07-08 00:00:00.000 |
|   1999 | Q3        |         7 | Jul         |    199907 | 1999-07-09 00:00:00.000 |
|   1999 | Q3        |         7 | Jul         |    199907 | 1999-07-10 00:00:00.000 |


Table: Geography
|   Zip | City   | State   | Region   | District             | Country   |
|------:|:-------|:--------|:---------|:---------------------|:----------|
| 68274 |        | Oaxaca  |          | Oaxaca de Juarez     | Mexico    |
| 68275 |        | Oaxaca  |          | Oaxaca de Juarez     | Mexico    |
| 68276 |        | Oaxaca  |          | Oaxaca de Juarez     | Mexico    |
| 71512 |        | Oaxaca  |          | Ocotlan de Morelos   | Mexico    |
| 71513 |        | Oaxaca  |          | Ocotlan de Morelos   | Mexico    |
| 71515 |        | Oaxaca  |          | Ocotlan de Morelos   | Mexico    |
| 71565 |        | Oaxaca  |          | La Pe                | Mexico    |
| 71566 |        | Oaxaca  |          | La Pe                | Mexico    |
| 71750 |        | Oaxaca  |          | Pinotepa de Don Luis | Mexico    |
| 70960 |        | Oaxaca  |          | Pluma Hidalgo        | Mexico    |


Table: Manufacturer
|   ManufacturerID | Manufacturer   |
|-----------------:|:---------------|
|                1 | Abbas          |
|                2 | Aliqui         |
|                3 | Barba          |
|                4 | Currus         |
|                5 | Fama           |
|                6 | Leo            |
|                7 | VanArsdel      |
|                8 | Natura         |
|                9 | Palma          |
|               10 | Pirum          |


Table: Product
|   ProductID | Product    | Category   | Segment     |   ManufacturerID |
|------------:|:-----------|:-----------|:------------|-----------------:|
|         536 | Maximus UC | Urban      | Convenience |                7 |
|         537 | Maximus UC | Urban      | Convenience |                7 |
|         538 | Maximus UC | Urban      | Convenience |                7 |
|         539 | Maximus UC | Urban      | Convenience |                7 |
|         540 | Maximus UC | Urban      | Convenience |                7 |
|         541 | Maximus UC | Urban      | Convenience |                7 |
|         542 | Maximus UC | Urban      | Convenience |                7 |
|         543 | Maximus UC | Urban      | Convenience |                7 |
|         544 | Maximus UC | Urban      | Convenience |                7 |
|         545 | Maximus UC | Urban      | Convenience |                7 |


Table: Sales
|   ProductID | Date                    |   Zip |   Units |   Revenue | Country   |
|------------:|:------------------------|------:|--------:|----------:|:----------|
|        1086 | 2003-09-18 00:00:00.000 | 94568 |       1 |   89.1975 | USA       |
|        1086 | 2003-09-19 00:00:00.000 | 92886 |       1 |   89.1975 | USA       |
|        1086 | 2003-08-27 00:00:00.000 | 50212 |       1 |   89.1975 | USA       |
|        1086 | 2003-07-25 00:00:00.000 | 91764 |       1 |   89.1975 | USA       |
|        1086 | 2004-04-30 00:00:00.000 | 34120 |       1 |   89.1975 | USA       |
|        1086 | 2003-07-25 00:00:00.000 | 95076 |       1 |   89.1975 | USA       |
|        1086 | 2004-05-02 00:00:00.000 |  2360 |       1 |   89.1975 | USA       |
|        1086 | 2003-06-12 00:00:00.000 | 96027 |       1 |   89.1975 | USA       |
|        1086 | 2004-04-11 00:00:00.000 | 87109 |       1 |   89.1975 | USA       |
|        1086 | 2004-06-30 00:00:00.000 | 94583 |       1 |   89.1975 | USA       |

 


tables: name: Date
data: Date.csv
name: Geography
data: Geography.csv
name: Manufacturer
data: Manufacturer.csv
name: Product
data: Product.csv
name: Sales
data: Sales.csv
label: Sales
ProductID
Product
ProductID
Product
ManufacturerID
Manufacturer
ManufacturerID
Sales
Date
Date
Date
Geography
Country
Sales
Country
output: {
    "joins": [
        {
            "from_table": "Sales",
            "from_column": "ProductID",
            "to_table": "Product",
            "to_column": "ProductID"
        },
        {
            "from_table": "Product",
            "from_column": "ManufacturerID",
            "to_table": "Manufacturer",
            "to_column": "ManufacturerID"
        },
        {
            "from_table": "Sales",
            "from_column": "Date",
            "to_table": "Date",
            "to_column": "Date"
        },
        {
            "from_table": "Geography",
            "from_column": "Country",
            "to_table": "Sales",
            "to_column": "Country"
        }
    ]
}