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: Calendar
| Date                    |
|:------------------------|
| 1950-01-01 00:00:00.000 |
| 1950-01-02 00:00:00.000 |
| 1950-01-03 00:00:00.000 |
| 1950-01-04 00:00:00.000 |
| 1950-01-05 00:00:00.000 |
| 1950-01-06 00:00:00.000 |
| 1950-01-07 00:00:00.000 |
| 1950-01-08 00:00:00.000 |
| 1950-01-09 00:00:00.000 |
| 1950-01-10 00:00:00.000 |


Table: codes-country-region
| Region-Code   |   Country-ID | region-full-names.Region-full-name   |
|:--------------|-------------:|:-------------------------------------|
| AS            |            4 | Asia                                 |
| EU            |            8 | Europe                               |
| EU            |           20 | Europe                               |
| AN            |           10 | Antarctica                           |
| AF            |           12 | Africa                               |
| AF            |           24 | Africa                               |
| OC            |           16 | Oceania                              |
| NA            |           28 | North America                        |
| EU            |           31 | Europe                               |
| SA            |           32 | South America                        |


Table: DIM-age
|   Age-ID | AgeGrp   |   AgeGrp-Max | Age-Category   |
|---------:|:---------|-------------:|:---------------|
|        1 | 0-4      |            4 | baby           |
|        2 | 5-9      |            9 | child          |
|        3 | 10-14    |           14 | child          |
|        4 | 15-19    |           19 | teen           |
|        5 | 20-24    |           24 | teen           |
|        6 | 25-29    |           29 | young adult    |
|        7 | 30-34    |           34 | young adult    |
|        8 | 35-39    |           39 | adult          |
|        9 | 40-44    |           44 | adult          |
|       10 | 45-49    |           49 | adult          |


Table: DIM-region
|   Country-ID | country-name        | Region-Code   | Region-full-name   |
|-------------:|:--------------------|:--------------|:-------------------|
|            4 | Afghanistan         | AS            | Asia               |
|            8 | Albania             | EU            | Europe             |
|           12 | Algeria             | AF            | Africa             |
|           24 | Angola              | AF            | Africa             |
|           28 | Antigua and Barbuda | NA            | North America      |
|           32 | Argentina           | SA            | South America      |
|           51 | Armenia             | EU            | Europe             |
|          533 | Aruba               | NA            | North America      |
|           36 | Australia           | OC            | Oceania            |
|           31 | Azerbaijan          | EU            | Europe             |


Table: FACT-population
|   Country-ID |   Age-ID | Year                    | Gender   |   Population |
|-------------:|---------:|:------------------------|:---------|-------------:|
|          428 |        1 | 1950-01-01 00:00:00.000 | Male     |        76001 |
|          428 |        1 | 1951-01-01 00:00:00.000 | Male     |        75356 |
|          428 |        1 | 1952-01-01 00:00:00.000 | Male     |        75490 |
|          428 |        1 | 1953-01-01 00:00:00.000 | Male     |        76115 |
|          428 |        1 | 1954-01-01 00:00:00.000 | Male     |        77131 |
|          428 |        1 | 1955-01-01 00:00:00.000 | Male     |        78608 |
|          428 |        1 | 1956-01-01 00:00:00.000 | Male     |        81116 |
|          428 |        1 | 1957-01-01 00:00:00.000 | Male     |        82669 |
|          428 |        1 | 1958-01-01 00:00:00.000 | Male     |        83651 |
|          428 |        1 | 1959-01-01 00:00:00.000 | Male     |        84525 |

 


tables: name: Calendar
data: Calendar.csv
name: codes-country-region
data: codes-country-region.csv
name: DIM-age
data: DIM-age.csv
name: DIM-region
data: DIM-region.csv
name: FACT-population
data: FACT-population.csv
label: FACT-population
Age-ID
DIM-age
Age-ID
FACT-population
Country-ID
DIM-region
Country-ID
output: {
    "joins": [
        {
            "from_table": "FACT-population",
            "from_column": "Age-ID",
            "to_table": "DIM-age",
            "to_column": "Age-ID"
        },
        {
            "from_table": "FACT-population",
            "from_column": "Country-ID",
            "to_table": "DIM-region",
            "to_column": "Country-ID"
        }
    ]
}