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: Clientes
|   IdCliente | Nombre   | Apellido   | Nacimiento              | EstadoCivil   | Género   |   Ingresos |   Hijos | Educación   | Ocupación   | País           | Ciudad         | IdCliente (clústeres) 2   |
|------------:|:---------|:-----------|:------------------------|:--------------|:---------|-----------:|--------:|:------------|:------------|:---------------|:---------------|:--------------------------|
|       21799 | Angel    | Cook       | 1950-09-27 00:00:00.000 | M             | M        |      60000 |       2 | EFP         | Profesional | Estados Unidos | Lebanon        | Clúster1                  |
|       21491 | Joseph   | Lewis      | 1950-04-12 00:00:00.000 | M             | M        |      60000 |       2 | EFP         | Profesional | Estados Unidos | Kirkland       | Clúster1                  |
|       25894 | Steve    | Zeng       | 1951-05-03 00:00:00.000 | M             | M        |      60000 |       2 | EFP         | Profesional | Estados Unidos | Burbank        | Clúster1                  |
|       28040 | Jack     | Mitchell   | 1949-02-22 00:00:00.000 | M             | M        |      60000 |       2 | EFP         | Profesional | Estados Unidos | Everett        | Clúster1                  |
|       15908 | David    | Wilson     | 1951-09-20 00:00:00.000 | M             | M        |      60000 |       2 | EFP         | Profesional | Estados Unidos | Imperial Beach | Clúster1                  |
|       19867 | Juan     | Sanchez    | 1948-12-20 00:00:00.000 | M             | M        |      60000 |       2 | EFP         | Profesional | Estados Unidos | Chula Vista    | Clúster1                  |
|       15343 | Carson   | Barnes     | 1951-09-07 00:00:00.000 | M             | M        |      60000 |       2 | EFP         | Profesional | Estados Unidos | Lakewood       | Clúster1                  |
|       12108 | James    | Walker     | 1950-02-23 00:00:00.000 | M             | M        |      60000 |       2 | EFP         | Profesional | Estados Unidos | Marysville     | Clúster1                  |
|       21498 | Cameron  | Wilson     | 1951-05-06 00:00:00.000 | M             | M        |      60000 |       2 | EFP         | Profesional | Estados Unidos | Coronado       | Clúster1                  |
|       12119 | Keith    | Raje       | 1951-06-01 00:00:00.000 | M             | M        |      60000 |       2 | EFP         | Profesional | Estados Unidos | Berkeley       | Clúster1                  |


Table: Clientes_ML
|   IDCliente | Estado Civil   | Género   |   Hijos |   Hijos en casa | Educación       | Ocupación   |   Casa |   Coches | Distancia   | Región   |   Comprador |   Edad |   Ingresos |
|------------:|:---------------|:---------|--------:|----------------:|:----------------|:------------|-------:|---------:|:------------|:---------|------------:|-------:|-----------:|
|       11471 | S              | F        |       0 |               0 | Partial College | Clerical    |      1 |        1 | 2-5 Miles   | Europe   |           0 |     36 |      30000 |
|       11600 | S              | F        |       0 |               0 | Partial College | Clerical    |      0 |        1 | 2-5 Miles   | Europe   |           0 |     32 |      30000 |
|       11601 | S              | M        |       0 |               0 | Partial College | Clerical    |      1 |        1 | 2-5 Miles   | Europe   |           1 |     32 |      30000 |
|       11602 | S              | M        |       0 |               0 | Partial College | Clerical    |      0 |        1 | 2-5 Miles   | Europe   |           0 |     32 |      30000 |
|       11604 | S              | M        |       0 |               0 | Partial College | Clerical    |      1 |        1 | 2-5 Miles   | Europe   |           1 |     32 |      30000 |
|       11614 | S              | F        |       0 |               0 | Partial College | Clerical    |      0 |        1 | 2-5 Miles   | Europe   |           1 |     34 |      30000 |
|       12373 | S              | M        |       0 |               0 | Partial College | Clerical    |      0 |        1 | 2-5 Miles   | Europe   |           1 |     30 |      30000 |
|       12378 | S              | F        |       0 |               0 | Partial College | Clerical    |      1 |        1 | 2-5 Miles   | Europe   |           1 |     30 |      30000 |
|       12379 | S              | F        |       0 |               0 | Partial College | Clerical    |      0 |        1 | 2-5 Miles   | Europe   |           0 |     31 |      30000 |
|       12515 | S              | F        |       0 |               0 | Partial College | Clerical    |      1 |        1 | 2-5 Miles   | Europe   |           0 |     32 |      30000 |


Table: Fechas
| Fecha                   |   Mes | Nombre del mes   |
|:------------------------|------:|:-----------------|
| 2016-01-01 00:00:00.000 |     1 | Enero            |
| 2016-01-02 00:00:00.000 |     1 | Enero            |
| 2016-01-03 00:00:00.000 |     1 | Enero            |
| 2016-01-04 00:00:00.000 |     1 | Enero            |
| 2016-01-05 00:00:00.000 |     1 | Enero            |
| 2016-01-06 00:00:00.000 |     1 | Enero            |
| 2016-01-07 00:00:00.000 |     1 | Enero            |
| 2016-01-08 00:00:00.000 |     1 | Enero            |
| 2016-01-09 00:00:00.000 |     1 | Enero            |
| 2016-01-10 00:00:00.000 |     1 | Enero            |


Table: Longitud Latitud Espan╠âa
| Comunidad     | Provincia   | Población               |   Latitud |   Longitud |   Altitud |   Habitantes |   Hombres |   Mujeres |
|:--------------|:------------|:------------------------|----------:|-----------:|----------:|-------------:|----------:|----------:|
| Castilla León | Burgos      | Abajas                  |   42.6235 |   -3.58023 |   837.949 |           26 |        16 |        10 |
| Castilla León | Burgos      | Adrada de Haza          |   41.5943 |   -3.82179 |   824.687 |          252 |       140 |       112 |
| Castilla León | Burgos      | Aguas Cándidas          |   42.7161 |   -3.50182 |   712.041 |           61 |        39 |        22 |
| Castilla León | Burgos      | Aguilar de Bureba       |   42.5901 |   -3.32874 |   689.226 |           70 |        43 |        27 |
| Castilla León | Burgos      | Albillos                |   42.2764 |   -3.79061 |   832.968 |          212 |       111 |       101 |
| Castilla León | Burgos      | Alcocero de Mola        |   42.4731 |   -3.35742 |   799.603 |           41 |        26 |        15 |
| Castilla León | Burgos      | Alfoz de Bricia         |   42.8967 |   -3.87917 |   847.356 |           97 |        51 |        46 |
| Castilla León | Burgos      | Alfoz de Quintanadueñas |   42.385  |   -3.73494 |   847.479 |         1787 |       967 |       820 |
| Castilla León | Burgos      | Alfoz de Santa Gadea    |   42.9556 |   -3.95056 |   882.318 |          115 |        67 |        48 |
| Castilla León | Burgos      | Altable                 |   42.6033 |   -3.07716 |   711.29  |           54 |        32 |        22 |


Table: Productos
|   IdProducto | Producto               | Color   | Precio Catálogo   | Tamaño   | Rango Tamaño   | Línea   | Modelo   | Subcategoría   | Categoría   | IdProducto (clústeres)   |
|-------------:|:-----------------------|:--------|:------------------|:---------|:---------------|:--------|:---------|:---------------|:------------|:-------------------------|
|          185 | HL Road Rim            | NA      |                   |          | NA             |         |          |                |             |                          |
|           81 | Keyed Washer           | NA      |                   |          | NA             |         |          |                |             |                          |
|           86 | External Lock Washer 7 | NA      |                   |          | NA             |         |          |                |             |                          |
|           91 | Internal Lock Washer 3 | NA      |                   |          | NA             |         |          |                |             |                          |
|          181 | ML Mountain Rim        | NA      |                   |          | NA             |         |          |                |             |                          |
|           96 | Internal Lock Washer 6 | NA      |                   |          | NA             |         |          |                |             |                          |
|            3 | BB Ball Bearing        | NA      |                   |          | NA             |         |          |                |             |                          |
|           20 | Fork End               | NA      |                   |          | NA             |         |          |                |             |                          |
|          101 | Thin-Jam Lock Nut 9    | NA      |                   |          | NA             |         |          |                |             |                          |
|          141 | Lower Head Race        | NA      |                   |          | NA             |         |          |                |             |                          |


Table: Ventas
|   CodProducto |   CodCliente |   Cantidad |   Precio |   Coste | Fecha                   |
|--------------:|-------------:|-----------:|---------:|--------:|:------------------------|
|           477 |        13458 |          1 |     4.99 |  1.8663 | 2018-08-08 00:00:00.000 |
|           477 |        11276 |          1 |     4.99 |  1.8663 | 2018-11-17 00:00:00.000 |
|           477 |        21863 |          1 |     4.99 |  1.8663 | 2018-10-07 00:00:00.000 |
|           477 |        11281 |          1 |     4.99 |  1.8663 | 2018-07-12 00:00:00.000 |
|           477 |        29478 |          1 |     4.99 |  1.8663 | 2018-12-25 00:00:00.000 |
|           477 |        11419 |          1 |     4.99 |  1.8663 | 2018-12-26 00:00:00.000 |
|           477 |        26895 |          1 |     4.99 |  1.8663 | 2018-12-26 00:00:00.000 |
|           477 |        18063 |          1 |     4.99 |  1.8663 | 2018-09-24 00:00:00.000 |
|           477 |        21849 |          1 |     4.99 |  1.8663 | 2018-09-20 00:00:00.000 |
|           477 |        21619 |          1 |     4.99 |  1.8663 | 2018-08-18 00:00:00.000 |

 


tables: name: Clientes
data: Clientes.csv
name: Clientes_ML
data: Clientes_ML.csv
name: Fechas
data: Fechas.csv
name: Longitud Latitud Espan╠âa
data: Longitud Latitud Espan╠âa.csv
name: Productos
data: Productos.csv
name: Ventas
data: Ventas.csv
label: Ventas
CodCliente
Clientes
IdCliente
Ventas
CodProducto
Productos
IdProducto
Ventas
Fecha
Fechas
Fecha
Clientes_ML
IDCliente
Clientes
IdCliente
output: {
    "joins": [
        {
            "from_table": "Ventas",
            "from_column": "CodCliente",
            "to_table": "Clientes",
            "to_column": "IdCliente"
        },
        {
            "from_table": "Ventas",
            "from_column": "CodProducto",
            "to_table": "Productos",
            "to_column": "IdProducto"
        },
        {
            "from_table": "Ventas",
            "from_column": "Fecha",
            "to_table": "Fechas",
            "to_column": "Fecha"
        },
        {
            "from_table": "Clientes_ML",
            "from_column": "IDCliente",
            "to_table": "Clientes",
            "to_column": "IdCliente"
        }
    ]
}