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: DIM_AGE_GROUP
|   age_group_id | age_group   |
|---------------:|:------------|
|              1 | 5-14 years  |
|              2 | 15-24 years |
|              3 | 25-34 years |
|              4 | 35-54 years |
|              5 | 55-74 years |
|              6 | 75+ years   |


Table: DIM_COUNTRIES
|   country_id | country_name   | country_region             | OECD_member   | country_code   |
|-------------:|:---------------|:---------------------------|:--------------|:---------------|
|            1 | Argentina      | Latin America & Caribbean  | Non-Member    | ARG            |
|            2 | Austria        | Europe & Central Asia      | Member        | AUT            |
|            3 | Bahamas        | Latin America & Caribbean  | Non-Member    | BHS            |
|            4 | Bahrain        | Middle East & North Africa | Non-Member    | BHR            |
|            5 | Barbados       | Latin America & Caribbean  | Non-Member    | BRB            |
|            6 | Belgium        | Europe & Central Asia      | Member        | BEL            |
|            7 | Belize         | Latin America & Caribbean  | Non-Member    | BLZ            |
|            8 | Brazil         | Latin America & Caribbean  | Non-Member    | BRA            |
|            9 | Bulgaria       | Europe & Central Asia      | Non-Member    | BGR            |
|           10 | Canada         | North America              | Member        | CAN            |


Table: DIM_DATE
| Date                    |
|:------------------------|
| 2004-12-31 00:00:00.000 |
| 2005-12-31 00:00:00.000 |
| 2006-12-31 00:00:00.000 |
| 2007-12-31 00:00:00.000 |
| 2008-12-31 00:00:00.000 |
| 2009-12-31 00:00:00.000 |
| 2010-12-31 00:00:00.000 |
| 2011-12-31 00:00:00.000 |
| 2012-12-31 00:00:00.000 |
| 2013-12-31 00:00:00.000 |


Table: DIM_GENDER
|   gender_id | gender_name   |
|------------:|:--------------|
|           1 | Male          |
|           2 | Female        |


Table: DIM_GENERATION
|   generation_id | generation_name   |
|----------------:|:------------------|
|               1 | Silent            |
|               2 | Boomers           |
|               3 | Generation X      |
|               4 | Millenials        |
|               5 | Generation Z      |


Table: FACT_GDP
|   gdp_id |   country_id |    gdp_for_year ($)  | date                    |
|---------:|-------------:|---------------------:|:------------------------|
|        1 |           10 |        1023196003075 | 2004-12-31 00:00:00.000 |
|       11 |           11 |          99210392858 | 2004-12-31 00:00:00.000 |
|       21 |            1 |         164657930453 | 2004-12-31 00:00:00.000 |
|       31 |           12 |         117074863822 | 2004-12-31 00:00:00.000 |
|       41 |           13 |          18529767934 | 2004-12-31 00:00:00.000 |
|       51 |           14 |          41574530816 | 2004-12-31 00:00:00.000 |
|       61 |           15 |          38203000000 | 2004-12-31 00:00:00.000 |
|       71 |           16 |          17422375000 | 2004-12-31 00:00:00.000 |
|       81 |           17 |         119162172468 | 2004-12-31 00:00:00.000 |
|       91 |           18 |         251373036671 | 2004-12-31 00:00:00.000 |


Table: FACT_STATISTICS
|   statisitcs_id |   country_id |   gender_id |   age_group_id |   suicides_no |   population |   generation_id | date                    |
|----------------:|-------------:|------------:|---------------:|--------------:|-------------:|----------------:|:------------------------|
|             168 |            2 |           2 |              1 |             0 |       433193 |               5 | 2007-12-31 00:00:00.000 |
|             283 |            3 |           2 |              1 |             0 |        28225 |               5 | 2007-12-31 00:00:00.000 |
|             286 |            3 |           1 |              1 |             0 |        28953 |               5 | 2007-12-31 00:00:00.000 |
|             296 |            3 |           2 |              1 |             0 |        27972 |               5 | 2008-12-31 00:00:00.000 |
|             307 |            3 |           2 |              1 |             0 |        27474 |               5 | 2009-12-31 00:00:00.000 |
|             310 |            3 |           1 |              1 |             0 |        28311 |               5 | 2009-12-31 00:00:00.000 |
|             321 |            3 |           2 |              1 |             0 |        26732 |               5 | 2010-12-31 00:00:00.000 |
|             324 |            3 |           1 |              1 |             0 |        27620 |               5 | 2010-12-31 00:00:00.000 |
|             331 |            3 |           2 |              1 |             0 |        26108 |               5 | 2011-12-31 00:00:00.000 |
|             335 |            3 |           1 |              1 |             0 |        27054 |               5 | 2011-12-31 00:00:00.000 |


Table: TBL_MEASURES
| Column   |
|----------|

 


tables: name: DIM_AGE_GROUP
data: DIM_AGE_GROUP.csv
name: DIM_COUNTRIES
data: DIM_COUNTRIES.csv
name: DIM_DATE
data: DIM_DATE.csv
name: DIM_GENDER
data: DIM_GENDER.csv
name: DIM_GENERATION
data: DIM_GENERATION.csv
name: FACT_GDP
data: FACT_GDP.csv
name: FACT_STATISTICS
data: FACT_STATISTICS.csv
name: TBL_MEASURES
data: TBL_MEASURES.csv
label: FACT_STATISTICS
age_group_id
DIM_AGE_GROUP
age_group_id
FACT_GDP
country_id
DIM_COUNTRIES
country_id
FACT_STATISTICS
country_id
DIM_COUNTRIES
country_id
FACT_STATISTICS
gender_id
DIM_GENDER
gender_id
FACT_STATISTICS
generation_id
DIM_GENERATION
generation_id
FACT_STATISTICS
date
DIM_DATE
Date
FACT_GDP
date
DIM_DATE
Date
output: {
    "joins": [
        {
            "from_table": "FACT_STATISTICS",
            "from_column": "age_group_id",
            "to_table": "DIM_AGE_GROUP",
            "to_column": "age_group_id"
        },
        {
            "from_table": "FACT_GDP",
            "from_column": "country_id",
            "to_table": "DIM_COUNTRIES",
            "to_column": "country_id"
        },
        {
            "from_table": "FACT_STATISTICS",
            "from_column": "country_id",
            "to_table": "DIM_COUNTRIES",
            "to_column": "country_id"
        },
        {
            "from_table": "FACT_STATISTICS",
            "from_column": "gender_id",
            "to_table": "DIM_GENDER",
            "to_column": "gender_id"
        },
        {
            "from_table": "FACT_STATISTICS",
            "from_column": "generation_id",
            "to_table": "DIM_GENERATION",
            "to_column": "generation_id"
        },
        {
            "from_table": "FACT_STATISTICS",
            "from_column": "date",
            "to_table": "DIM_DATE",
            "to_column": "Date"
        },
        {
            "from_table": "FACT_GDP",
            "from_column": "date",
            "to_table": "DIM_DATE",
            "to_column": "Date"
        }
    ]
}