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: DimAge
| iso   |   life_expectancy | median_age   | aged_65_older   | aged_70_older   |
|:------|------------------:|:-------------|:----------------|:----------------|
| AFG   |             64.83 | 18.6         | 2.581           | 1.337           |
| ALB   |             78.57 | 38           | 13.188          | 8.643           |
| DZA   |             76.88 | 29.1         | 6.211           | 3.857           |
| AND   |             83.73 |              |                 |                 |
| AGO   |             61.15 | 16.8         | 2.405           | 1.362           |
| ATG   |             77.02 | 32.1         | 6.933           | 4.631           |
| ARG   |             76.67 | 31.9         | 11.198          | 7.441           |
| ARM   |             75.09 | 35.7         | 11.232          | 7.571           |
| AUS   |             83.44 | 37.9         | 15.504          | 10.129          |
| AUT   |             81.54 | 44.4         | 19.202          | 13.748          |


Table: DimCalendar
| Date                    |   Year |   Monthnumber | YearMonthShort   | MonthNameShort   |   Weeknumber | DayOfWeekShort   | Quarter   |   DateAsInteger |
|:------------------------|-------:|--------------:|:-----------------|:-----------------|-------------:|:-----------------|:----------|----------------:|
| 2020-07-01 00:00:00.000 |   2020 |             7 | 2020/Jul         | Jul              |           27 | Wed              | Q3        |        20200701 |
| 2020-07-02 00:00:00.000 |   2020 |             7 | 2020/Jul         | Jul              |           27 | Thu              | Q3        |        20200702 |
| 2020-07-03 00:00:00.000 |   2020 |             7 | 2020/Jul         | Jul              |           27 | Fri              | Q3        |        20200703 |
| 2020-07-04 00:00:00.000 |   2020 |             7 | 2020/Jul         | Jul              |           27 | Sat              | Q3        |        20200704 |
| 2020-07-05 00:00:00.000 |   2020 |             7 | 2020/Jul         | Jul              |           28 | Sun              | Q3        |        20200705 |
| 2020-07-06 00:00:00.000 |   2020 |             7 | 2020/Jul         | Jul              |           28 | Mon              | Q3        |        20200706 |
| 2020-07-07 00:00:00.000 |   2020 |             7 | 2020/Jul         | Jul              |           28 | Tue              | Q3        |        20200707 |
| 2020-07-08 00:00:00.000 |   2020 |             7 | 2020/Jul         | Jul              |           28 | Wed              | Q3        |        20200708 |
| 2020-07-09 00:00:00.000 |   2020 |             7 | 2020/Jul         | Jul              |           28 | Thu              | Q3        |        20200709 |
| 2020-07-10 00:00:00.000 |   2020 |             7 | 2020/Jul         | Jul              |           28 | Fri              | Q3        |        20200710 |


Table: DimContinent
| name          |
|:--------------|
| Asia          |
| Europe        |
| Africa        |
| North America |
| South America |
| Oceania       |


Table: DimCountry
| iso   | continent     | country             |
|:------|:--------------|:--------------------|
| AFG   | Asia          | Afghanistan         |
| ALB   | Europe        | Albania             |
| DZA   | Africa        | Algeria             |
| AND   | Europe        | Andorra             |
| AGO   | Africa        | Angola              |
| ATG   | North America | Antigua and Barbuda |
| ARG   | South America | Argentina           |
| ARM   | Asia          | Armenia             |
| AUS   | Oceania       | Australia           |
| AUT   | Europe        | Austria             |


Table: DimDemographics
| iso   | gdp_per_capita   | extreme_poverty   |   cardiovasc_death_rate |   diabetes_prevalence | handwashing_facilities   | hospital_beds_per_thousand   |   human_development_index |
|:------|:-----------------|:------------------|------------------------:|----------------------:|:-------------------------|:-----------------------------|--------------------------:|
| ALB   | 11803.431        | 1.1               |                 304.195 |                 10.08 |                          | 2.89                         |                     0.795 |
| AND   |                  |                   |                 109.135 |                  7.97 |                          |                              |                     0.868 |
| ATG   | 21490.943        |                   |                 191.511 |                 13.17 |                          | 3.8                          |                     0.778 |
| ARG   | 18933.907        | 0.6               |                 191.032 |                  5.5  |                          | 5                            |                     0.845 |
| AUS   | 44648.71         | 0.5               |                 107.791 |                  5.07 |                          | 3.84                         |                     0.944 |
| AUT   | 45436.686        | 0.7               |                 145.183 |                  6.35 |                          | 7.37                         |                     0.922 |
| BHS   | 27717.847        |                   |                 235.954 |                 13.17 |                          | 2.9                          |                     0.814 |
| BHR   | 43290.705        |                   |                 151.689 |                 16.52 |                          | 2                            |                     0.852 |
| BLR   | 17167.967        |                   |                 443.129 |                  5.18 |                          | 11                           |                     0.823 |
| BEL   | 42658.576        | 0.2               |                 114.898 |                  4.29 |                          | 5.64                         |                     0.931 |


Table: DimPopulation
| iso   |    value |   density | densityLvl   |   life_expectancy |
|:------|---------:|----------:|:-------------|------------------:|
| AFG   | 38928341 |        54 | low          |             64.83 |
| ALB   |  2877800 |       105 | low          |             78.57 |
| DZA   | 43851043 |        17 | low          |             76.88 |
| AND   |    77265 |       164 | low          |             83.73 |
| AGO   | 32866268 |        24 | low          |             61.15 |
| ATG   |    97928 |       232 | mid          |             77.02 |
| ARG   | 45195777 |        16 | low          |             76.67 |
| ARM   |  2963234 |       103 | low          |             75.09 |
| AUS   | 25499881 |         3 | low          |             83.44 |
| AUT   |  9006400 |       107 | low          |             81.54 |


Table: FactConfirmed
| Date                    |   Case | iso   | Country/Region   |   DailyCase |
|:------------------------|-------:|:------|:-----------------|------------:|
| 2020-01-22 00:00:00.000 |      0 | WSM   | Samoa            |  9337608167 |
| 2020-01-23 00:00:00.000 |      0 | WSM   | Samoa            |  9337608167 |
| 2020-01-24 00:00:00.000 |      0 | WSM   | Samoa            |  9337608167 |
| 2020-01-25 00:00:00.000 |      0 | WSM   | Samoa            |  9337608167 |
| 2020-01-26 00:00:00.000 |      0 | WSM   | Samoa            |  9337608167 |
| 2020-01-27 00:00:00.000 |      0 | WSM   | Samoa            |  9337608167 |
| 2020-01-28 00:00:00.000 |      0 | WSM   | Samoa            |  9337608167 |
| 2020-01-29 00:00:00.000 |      0 | WSM   | Samoa            |  9337608167 |
| 2020-01-30 00:00:00.000 |      0 | WSM   | Samoa            |  9337608167 |
| 2020-01-31 00:00:00.000 |      0 | WSM   | Samoa            |  9337608167 |


Table: FactDeaths
| Date                    |   Case | iso   | Country/Region   |
|:------------------------|-------:|:------|:-----------------|
| 2020-01-22 00:00:00.000 |      0 | KHM   | Cambodia         |
| 2020-01-23 00:00:00.000 |      0 | KHM   | Cambodia         |
| 2020-01-24 00:00:00.000 |      0 | KHM   | Cambodia         |
| 2020-01-25 00:00:00.000 |      0 | KHM   | Cambodia         |
| 2020-01-26 00:00:00.000 |      0 | KHM   | Cambodia         |
| 2020-01-27 00:00:00.000 |      0 | KHM   | Cambodia         |
| 2020-01-28 00:00:00.000 |      0 | KHM   | Cambodia         |
| 2020-01-29 00:00:00.000 |      0 | KHM   | Cambodia         |
| 2020-01-30 00:00:00.000 |      0 | KHM   | Cambodia         |
| 2020-01-31 00:00:00.000 |      0 | KHM   | Cambodia         |


Table: FactRecovered
| Country/Region   | Date                    |   Case | iso   |
|:-----------------|:------------------------|-------:|:------|
| Sweden           | 2020-01-22 00:00:00.000 |      0 | SWE   |
| Sweden           | 2020-01-23 00:00:00.000 |      0 | SWE   |
| Sweden           | 2020-01-24 00:00:00.000 |      0 | SWE   |
| Sweden           | 2020-01-25 00:00:00.000 |      0 | SWE   |
| Sweden           | 2020-01-26 00:00:00.000 |      0 | SWE   |
| Sweden           | 2020-01-27 00:00:00.000 |      0 | SWE   |
| Sweden           | 2020-01-28 00:00:00.000 |      0 | SWE   |
| Sweden           | 2020-01-29 00:00:00.000 |      0 | SWE   |
| Sweden           | 2020-01-30 00:00:00.000 |      0 | SWE   |
| Sweden           | 2020-01-31 00:00:00.000 |      0 | SWE   |


Table: FactResponse
| iso   | date                    | reproduction_rate   | stringency_index   |
|:------|:------------------------|:--------------------|:-------------------|
| VAT   | 2020-03-06 00:00:00.000 |                     |                    |
| VAT   | 2020-03-07 00:00:00.000 |                     |                    |
| VAT   | 2020-03-08 00:00:00.000 |                     |                    |
| VAT   | 2020-03-09 00:00:00.000 |                     |                    |
| VAT   | 2020-03-10 00:00:00.000 |                     |                    |
| VAT   | 2020-03-11 00:00:00.000 |                     |                    |
| VAT   | 2020-03-12 00:00:00.000 |                     |                    |
| VAT   | 2020-03-13 00:00:00.000 |                     |                    |
| VAT   | 2020-03-14 00:00:00.000 |                     |                    |
| VAT   | 2020-03-15 00:00:00.000 |                     |                    |

 


tables: name: DimAge
data: DimAge.csv
name: DimCalendar
data: DimCalendar.csv
name: DimContinent
data: DimContinent.csv
name: DimCountry
data: DimCountry.csv
name: DimDemographics
data: DimDemographics.csv
name: DimPopulation
data: DimPopulation.csv
name: FactConfirmed
data: FactConfirmed.csv
name: FactDeaths
data: FactDeaths.csv
name: FactRecovered
data: FactRecovered.csv
name: FactResponse
data: FactResponse.csv
label: DimCountry
iso
DimPopulation
iso
DimCountry
continent
DimContinent
name
FactConfirmed
iso
DimCountry
iso
FactDeaths
iso
DimCountry
iso
FactRecovered
iso
DimCountry
iso
FactResponse
iso
DimCountry
iso
DimCountry
iso
DimDemographics
iso
DimAge
iso
DimPopulation
iso
output: {
    "joins": [
        {
            "from_table": "DimCountry",
            "from_column": "iso",
            "to_table": "DimPopulation",
            "to_column": "iso"
        },
        {
            "from_table": "DimCountry",
            "from_column": "continent",
            "to_table": "DimContinent",
            "to_column": "name"
        },
        {
            "from_table": "FactConfirmed",
            "from_column": "iso",
            "to_table": "DimCountry",
            "to_column": "iso"
        },
        {
            "from_table": "FactDeaths",
            "from_column": "iso",
            "to_table": "DimCountry",
            "to_column": "iso"
        },
        {
            "from_table": "FactRecovered",
            "from_column": "iso",
            "to_table": "DimCountry",
            "to_column": "iso"
        },
        {
            "from_table": "FactResponse",
            "from_column": "iso",
            "to_table": "DimCountry",
            "to_column": "iso"
        },
        {
            "from_table": "DimCountry",
            "from_column": "iso",
            "to_table": "DimDemographics",
            "to_column": "iso"
        },
        {
            "from_table": "DimAge",
            "from_column": "iso",
            "to_table": "DimPopulation",
            "to_column": "iso"
        }
    ]
}