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: Country
| Country Code   | Country     |
|:---------------|:------------|
| AUS            | Australia   |
| AUT            | Austria     |
| BEL            | Belgium     |
| CAN            | Canada      |
| CHE            | Switzerland |
| DEU            | Germany     |
| DNK            | Denmark     |
| FRA            | France      |
| GBR            | UK          |
| IRL            | Ireland     |


Table: Dates
| Date                    |   Year |   MonthNumber | MonthName   |   Day |   WeekdayNumber | WeekdayName   | IsWeekend   |
|:------------------------|-------:|--------------:|:------------|------:|----------------:|:--------------|:------------|
| 2016-01-01 00:00:00.000 |   2016 |             1 | Jan         |     1 |               5 | Fri           | No          |
| 2016-01-02 00:00:00.000 |   2016 |             1 | Jan         |     2 |               6 | Sat           | Yes         |
| 2016-01-03 00:00:00.000 |   2016 |             1 | Jan         |     3 |               7 | Sun           | Yes         |
| 2016-01-04 00:00:00.000 |   2016 |             1 | Jan         |     4 |               1 | Mon           | No          |
| 2016-01-05 00:00:00.000 |   2016 |             1 | Jan         |     5 |               2 | Tue           | No          |
| 2016-01-06 00:00:00.000 |   2016 |             1 | Jan         |     6 |               3 | Wed           | No          |
| 2016-01-07 00:00:00.000 |   2016 |             1 | Jan         |     7 |               4 | Thu           | No          |
| 2016-01-08 00:00:00.000 |   2016 |             1 | Jan         |     8 |               5 | Fri           | No          |
| 2016-01-09 00:00:00.000 |   2016 |             1 | Jan         |     9 |               6 | Sat           | Yes         |
| 2016-01-10 00:00:00.000 |   2016 |             1 | Jan         |    10 |               7 | Sun           | Yes         |


Table: Location
|   Location ID | Location   | Country Code   |
|--------------:|:-----------|:---------------|
|             1 | Melbourne  | AUS            |
|             2 | Canberra   | AUS            |
|             3 | Sydney     | AUS            |
|             4 | Graz       | AUT            |
|             5 | Salzburg   | AUT            |
|             6 | Brussels   | BEL            |
|             7 | Vancouver  | CAN            |
|             8 | Toronto    | CAN            |
|             9 | Geneva     | CHE            |
|            10 | Lausanne   | CHE            |


Table: Products
|   ID | Product            | Category   |
|-----:|:-------------------|:-----------|
|    1 | Coffee             | Beverages  |
|    2 | Tea                | Beverages  |
|    3 | Green Tea          | Beverages  |
|    4 | Lemon & Ginger Tea | Beverages  |
|    5 | Apple Juice        | Beverages  |
|    6 | Peach Juice        | Beverages  |
|    7 | Tomato Juice       | Beverages  |
|    8 | Orange Juice       | Beverages  |
|    9 | Still Water        | Beverages  |
|   10 | Sparkling Water    | Beverages  |


Table: Sales
|   Order ID | Date                    |   Product ID |   Location ID |   Amount |
|-----------:|:------------------------|-------------:|--------------:|---------:|
|       7371 | 2016-01-14 00:00:00.000 |           12 |            12 |     1.68 |
|      12262 | 2016-01-22 00:00:00.000 |           12 |            12 |     4.26 |
|      15074 | 2016-01-27 00:00:00.000 |           12 |            12 |     2.4  |
|      15447 | 2016-01-28 00:00:00.000 |           12 |            12 |     1.75 |
|      18254 | 2016-02-02 00:00:00.000 |           12 |            12 |     4.24 |
|      20536 | 2016-02-06 00:00:00.000 |           12 |            12 |     4.48 |
|      21004 | 2016-02-06 00:00:00.000 |           12 |            12 |     5.1  |
|      22968 | 2016-02-10 00:00:00.000 |           12 |            12 |     2.9  |
|      23670 | 2016-02-11 00:00:00.000 |           12 |            12 |     4    |
|      25248 | 2016-02-14 00:00:00.000 |           12 |            12 |     1.81 |

 


tables: name: Country
data: Country.csv
name: Dates
data: Dates.csv
name: Location
data: Location.csv
name: Products
data: Products.csv
name: Sales
data: Sales.csv
label: Sales
Product ID
Products
ID
Sales
Location ID
Location
Location ID
Location
Country Code
Country
Country Code
output: {
    "joins": [
        {
            "from_table": "Sales",
            "from_column": "Product ID",
            "to_table": "Products",
            "to_column": "ID"
        },
        {
            "from_table": "Sales",
            "from_column": "Location ID",
            "to_table": "Location",
            "to_column": "Location ID"
        },
        {
            "from_table": "Location",
            "from_column": "Country Code",
            "to_table": "Country",
            "to_column": "Country Code"
        }
    ]
}