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: Cases_Deaths
| date                    |   new_cases |   new_deaths |   total_cases |   total_deaths |   ID_Country |   Index |
|:------------------------|------------:|-------------:|--------------:|---------------:|-------------:|--------:|
| 2020-11-11 00:00:00.000 |           0 |            0 |             1 |              0 |           51 |   46885 |
| 2020-11-12 00:00:00.000 |           0 |            0 |             1 |              0 |           51 |   47074 |
| 2020-11-13 00:00:00.000 |           0 |            0 |             1 |              0 |           51 |   47263 |
| 2020-11-14 00:00:00.000 |           0 |            0 |             1 |              0 |           51 |   47452 |
| 2020-11-15 00:00:00.000 |           0 |            0 |             1 |              0 |           51 |   47641 |
| 2020-11-16 00:00:00.000 |           0 |            0 |             1 |              0 |           51 |   47830 |
| 2020-11-17 00:00:00.000 |           0 |            0 |             1 |              0 |           51 |   48019 |
| 2020-11-18 00:00:00.000 |           0 |            0 |             1 |              0 |           51 |   48208 |
| 2020-11-19 00:00:00.000 |           0 |            0 |             1 |              0 |           51 |   48398 |
| 2020-11-20 00:00:00.000 |           0 |            0 |             1 |              0 |           51 |   48588 |


Table: excess_mortality
| Date                    |   ID_Country |   Deaths_all_Reasons |   Index |
|:------------------------|-------------:|---------------------:|--------:|
| 2021-04-01 00:00:00.000 |            5 |                    0 |     900 |
| 2021-04-01 00:00:00.000 |            5 |                    0 |     901 |
| 2021-04-01 00:00:00.000 |            7 |                    0 |     902 |
| 2021-04-01 00:00:00.000 |            7 |                    0 |     903 |
| 2021-04-01 00:00:00.000 |            9 |                    0 |     904 |
| 2021-04-01 00:00:00.000 |            9 |                    0 |     905 |
| 2021-04-01 00:00:00.000 |           12 |                    0 |     906 |
| 2021-04-01 00:00:00.000 |           12 |                    0 |     907 |
| 2021-04-01 00:00:00.000 |           14 |                    0 |     908 |
| 2021-04-01 00:00:00.000 |           14 |                    0 |     909 |


Table: Last Refresh Date
| Last Refresh Date       |
|:------------------------|
| 2022-08-18 08:21:01.583 |


Table: Locations
| Country/Region   | location         | continent     |   latitude |   longitude |   ID_Country |   population_year |   population |
|:-----------------|:-----------------|:--------------|-----------:|------------:|-------------:|------------------:|-------------:|
| Afghanistan      | Afghanistan      | Asia          |   33.9391  |    67.71    |            1 |              2020 |     38928341 |
| Saint Lucia      | Saint Lucia      | North America |   13.9094  |   -60.9789  |            2 |              2020 |       183629 |
| Norway           | Norway           | Europe        |   60.472   |     8.46895 |            3 |              2020 |      5421242 |
| Sri Lanka        | Sri Lanka        | Asia          |    7.87305 |    80.7718  |            4 |              2020 |     21413250 |
| Oman             | Oman             | Asia          |   21.5126  |    55.9233  |            5 |              2020 |      5106622 |
| Pakistan         | Pakistan         | Asia          |   30.3753  |    69.3451  |            6 |              2020 |    220892331 |
| Panama           | Panama           | North America |    8.53798 |   -80.7821  |            7 |              2020 |      4314768 |
| Papua New Guinea | Papua New Guinea | Oceania       |   -6.31499 |   143.956   |            8 |              2020 |      8947027 |
| Paraguay         | Paraguay         | South America |  -23.4425  |   -58.4438  |            9 |              2020 |      7132530 |
| Peru             | Peru             | South America |   -9.18997 |   -75.0152  |           10 |              2020 |     32971846 |


Table: Map Measures
| Name                  |   Ordinal |
|:----------------------|----------:|
| Total Cases           |        -1 |
| Total Deaths          |        -1 |
| Total Vaccinations    |        -1 |
| Average Cases Per Day |        -1 |


Table: Measure
| Dummy   |
|:--------|
| Dummy   |


Table: Time
| Date                    |   Year |   Month_Number | Month   |   Day_Number | Day       | Year-Month   |     VO |
|:------------------------|-------:|---------------:|:--------|-------------:|:----------|:-------------|-------:|
| 2020-01-01 00:00:00.000 |   2020 |              1 | January |            1 | Wednesday | 2020-January | 202001 |
| 2020-01-02 00:00:00.000 |   2020 |              1 | January |            2 | Thursday  | 2020-January | 202001 |
| 2020-01-03 00:00:00.000 |   2020 |              1 | January |            3 | Friday    | 2020-January | 202001 |
| 2020-01-04 00:00:00.000 |   2020 |              1 | January |            4 | Saturday  | 2020-January | 202001 |
| 2020-01-05 00:00:00.000 |   2020 |              1 | January |            5 | Sunday    | 2020-January | 202001 |
| 2020-01-06 00:00:00.000 |   2020 |              1 | January |            6 | Monday    | 2020-January | 202001 |
| 2020-01-07 00:00:00.000 |   2020 |              1 | January |            7 | Tuesday   | 2020-January | 202001 |
| 2020-01-08 00:00:00.000 |   2020 |              1 | January |            8 | Wednesday | 2020-January | 202001 |
| 2020-01-09 00:00:00.000 |   2020 |              1 | January |            9 | Thursday  | 2020-January | 202001 |
| 2020-01-10 00:00:00.000 |   2020 |              1 | January |           10 | Friday    | 2020-January | 202001 |


Table: vaccinations
| date                    |   people_vaccinated |   people_fully_vaccinated |   ID_Country |   total_vaccinations |   Index |
|:------------------------|--------------------:|--------------------------:|-------------:|---------------------:|--------:|
| 2021-04-14 00:00:00.000 |                   0 |                         0 |           63 |                    0 |    5115 |
| 2021-03-17 00:00:00.000 |                   0 |                         0 |           63 |                    0 |    5129 |
| 2021-03-17 00:00:00.000 |                   0 |                         0 |           63 |                    0 |    5131 |
| 2021-04-15 00:00:00.000 |                   0 |                         0 |           63 |                    0 |    5135 |
| 2021-01-24 00:00:00.000 |                   0 |                         0 |           63 |                    0 |    5140 |
| 2021-01-23 00:00:00.000 |                   0 |                         0 |           63 |                    0 |    5141 |
| 2021-03-16 00:00:00.000 |                   0 |                         0 |           63 |                    0 |    5143 |
| 2021-02-11 00:00:00.000 |                   0 |                         0 |           63 |                    0 |    5147 |
| 2021-02-11 00:00:00.000 |                   0 |                         0 |           63 |                    0 |    5151 |
| 2021-04-14 00:00:00.000 |                   0 |                         0 |           63 |                    0 |    5157 |

 


tables: name: Cases_Deaths
data: Cases_Deaths.csv
name: excess_mortality
data: excess_mortality.csv
name: Last Refresh Date
data: Last Refresh Date.csv
name: Locations
data: Locations.csv
name: Map Measures
data: Map Measures.csv
name: Measure
data: Measure.csv
name: Time
data: Time.csv
name: vaccinations
data: vaccinations.csv
label: Cases_Deaths
ID_Country
Locations
ID_Country
excess_mortality
ID_Country
Locations
ID_Country
vaccinations
ID_Country
Locations
ID_Country
output: {
    "joins": [
        {
            "from_table": "Cases_Deaths",
            "from_column": "ID_Country",
            "to_table": "Locations",
            "to_column": "ID_Country"
        },
        {
            "from_table": "excess_mortality",
            "from_column": "ID_Country",
            "to_table": "Locations",
            "to_column": "ID_Country"
        },
        {
            "from_table": "vaccinations",
            "from_column": "ID_Country",
            "to_table": "Locations",
            "to_column": "ID_Country"
        }
    ]
}