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: customer
|   customer_id | name                 |
|--------------:|:---------------------|
|             1 | Joshia Whear         |
|             2 | Christabel Matherson |
|             3 | Emile Airds          |
|             4 | Ermentrude Lancaster |
|             5 | Dylan Flacke         |
|             6 | Valera Petford       |
|             7 | Caye Windibank       |
|             8 | Barty Gallon         |
|             9 | Albie Pastor         |
|            10 | Fabiano Stedman      |


Table: DateTable
| Date                    |   DateAsInteger |   Year |   MonthNo | YearMonthNo   | YearMonth   | MonthShort   | MonthLong   |   WeekNo | WeekDay   | WeekDayShort   | Quarter   | YearQuarter   |
|:------------------------|----------------:|-------:|----------:|:--------------|:------------|:-------------|:------------|---------:|:----------|:---------------|:----------|:--------------|
| 2018-07-01 00:00:00.000 |        20180701 |   2018 |         7 | 2018/07       | 2018/Jul    | Jul          | July        |        1 | Sunday    | Sun            | Q3        | 2018/Q3       |
| 2018-07-02 00:00:00.000 |        20180702 |   2018 |         7 | 2018/07       | 2018/Jul    | Jul          | July        |        2 | Monday    | Mon            | Q3        | 2018/Q3       |
| 2018-07-03 00:00:00.000 |        20180703 |   2018 |         7 | 2018/07       | 2018/Jul    | Jul          | July        |        3 | Tuesday   | Tue            | Q3        | 2018/Q3       |
| 2018-07-04 00:00:00.000 |        20180704 |   2018 |         7 | 2018/07       | 2018/Jul    | Jul          | July        |        4 | Wednesday | Wed            | Q3        | 2018/Q3       |
| 2018-07-05 00:00:00.000 |        20180705 |   2018 |         7 | 2018/07       | 2018/Jul    | Jul          | July        |        5 | Thursday  | Thu            | Q3        | 2018/Q3       |
| 2018-07-06 00:00:00.000 |        20180706 |   2018 |         7 | 2018/07       | 2018/Jul    | Jul          | July        |        6 | Friday    | Fri            | Q3        | 2018/Q3       |
| 2018-07-07 00:00:00.000 |        20180707 |   2018 |         7 | 2018/07       | 2018/Jul    | Jul          | July        |        7 | Saturday  | Sat            | Q3        | 2018/Q3       |
| 2018-07-08 00:00:00.000 |        20180708 |   2018 |         7 | 2018/07       | 2018/Jul    | Jul          | July        |        1 | Sunday    | Sun            | Q3        | 2018/Q3       |
| 2018-07-09 00:00:00.000 |        20180709 |   2018 |         7 | 2018/07       | 2018/Jul    | Jul          | July        |        2 | Monday    | Mon            | Q3        | 2018/Q3       |
| 2018-07-10 00:00:00.000 |        20180710 |   2018 |         7 | 2018/07       | 2018/Jul    | Jul          | July        |        3 | Tuesday   | Tue            | Q3        | 2018/Q3       |


Table: employee
|   employee_id | name                |
|--------------:|:--------------------|
|             1 | Lizbeth Dare        |
|             2 | Christie Giacomelli |
|             3 | Sigfried Saiz       |
|             4 | Anneliese McComas   |
|             5 | Vladamir Elfleet    |
|             6 | Harris Mahomet      |
|             7 | Burl Hemeret        |
|             8 | Yevette McNerlin    |
|             9 | Baird Behnecken     |
|            10 | Hayward Creamer     |


Table: location
|   id | city             | countries     |   zip code |     latitude |      longitude | state         |
|-----:|:-----------------|:--------------|-----------:|-------------:|---------------:|:--------------|
|    1 | Marietta         | United States |      30061 |  3.39491e+08 |   -8.45507e+08 | Georgia       |
|    2 | Ogden            | United States |      84409 | 41.2         | -111.98        | Utah          |
|    3 | Migrate          | United States |      41905 |  3.83238e+08 |   -1.08182e+09 | Kentucky      |
|    4 | Colorado Springs | United States |      80905 |  3.88163e+08 |   -1.04851e+09 | Colorado      |
|    5 | Port Washington  | United States |      11054 |  4.08395e+08 |   -7.37024e+08 | New York      |
|    6 | Tucson           | United States |      85715 |  3.22515e+08 |   -1.10837e+09 | Arizona       |
|    7 | Pensacola        | United States |      32595 |  3.04757e+07 |   -8.71936e+07 | Florida       |
|    8 | Erie             | United States |      16505 |  4.20949e+08 |   -8.01708e+08 | Pennsylvania  |
|    9 | Houston          | United States |      77090 |  3.00119e+08 |   -9.54463e+08 | Texas         |
|   10 | Huntington       | United States |      25709 |  3.841e+08   |   -8.246e+08   | West Virginia |


Table: products
|   product_id | product                          |   price |   profit margine |   tax rate |
|-------------:|:---------------------------------|--------:|-----------------:|-----------:|
|            1 | Chips - Potato Jalapeno          |    4.29 |             0.24 |       0.07 |
|            2 | Iced Tea - Lemon, 460 Ml         |    2.69 |             0.22 |       0.19 |
|            3 | Beer - Sleemans Cream Ale        |    3.39 |             0.26 |       0.07 |
|            4 | Icecream - Dstk Cml And Fdg      |    2.59 |             0.14 |       0.19 |
|            5 | Onions - Spanish                 |    3.99 |             0.15 |       0.19 |
|            6 | Cactus Pads                      |    3.39 |             0.19 |       0.19 |
|            7 | Cheese - Colby                   |    3.99 |             0.18 |       0.19 |
|            8 | Beef - Ground, Extra Lean, Fresh |    1.89 |             0.28 |       0.19 |
|            9 | Bar Mix - Lemon                  |    2.29 |             0.18 |       0.07 |
|           10 | Nori Sea Weed                    |    3.89 |             0.13 |       0.07 |


Table: sales
|   id |   customer_id |   location_id |   product_id |   employee_id | date                    |   quantity |   price |   revenue |
|-----:|--------------:|--------------:|-------------:|--------------:|:------------------------|-----------:|--------:|----------:|
|   35 |           547 |             5 |           66 |            75 | 2019-09-25 00:00:00.000 |          1 |    3.39 |      3.39 |
|  402 |           164 |             5 |           67 |            72 | 2019-10-30 00:00:00.000 |          1 |    7.99 |      7.99 |
|  419 |           440 |             5 |           61 |            80 | 2019-07-03 00:00:00.000 |          1 |    6.99 |      6.99 |
|  504 |            53 |             5 |           59 |            73 | 2019-06-05 00:00:00.000 |          1 |    1.99 |      1.99 |
|  833 |           277 |             5 |           64 |            71 | 2020-02-07 00:00:00.000 |          1 |    3.39 |      3.39 |
|  837 |           179 |             5 |           60 |            47 | 2019-10-22 00:00:00.000 |          1 |    3.49 |      3.49 |
|  903 |           246 |             5 |            9 |            63 | 2019-05-21 00:00:00.000 |          1 |    2.29 |      2.29 |
|  961 |           118 |             5 |           75 |            77 | 2020-04-16 00:00:00.000 |          1 |    5.49 |      5.49 |
|  963 |           532 |             5 |           71 |            56 | 2020-03-13 00:00:00.000 |          1 |    6.99 |      6.99 |
| 1050 |           201 |             5 |           59 |            72 | 2020-04-10 00:00:00.000 |          1 |    1.99 |      1.99 |


Table: SampleFilterTable
|   product_id | product                        |   price |   profit margine |   tax rate |
|-------------:|:-------------------------------|--------:|-----------------:|-----------:|
|           11 | Pasta - Rotini, Colour, Dry    |    5.49 |             0.28 |       0.19 |
|           13 | Chips - Assorted               |    5.49 |             0.11 |       0.07 |
|           17 | Stainless Steel Cleaner Vision |    5.99 |             0.16 |       0.19 |
|           19 | Shichimi Togarashi Peppeers    |    6.99 |             0.21 |       0.07 |
|           23 | Soup - Clam Chowder, Dry Mix   |    6.99 |             0.27 |       0.07 |
|           40 | Mangostein                     |    5.99 |             0.19 |       0.07 |
|           48 | Oil - Olive, Extra Virgin      |    6.99 |             0.11 |       0.07 |
|           49 | Tequila - Sauza Silver         |    6.99 |             0.19 |       0.07 |
|           52 | Dill - Primerba, Paste         |    5.99 |             0.27 |       0.19 |
|           61 | Lid Coffeecup 12oz D9542b      |    6.99 |             0.28 |       0.07 |


Table: SampleTable
| Sample Column   | Sample Column2   | Sample Column1   |
|:----------------|:-----------------|:-----------------|
|                 | False            |                  |
|                 | False            |                  |
| 1               | True             | 1                |
| 1               | True             | 1                |
| 2               | True             | 2                |
| 2               | True             | 2                |
| 3               | True             | 3                |
| text1           | True             | text1            |
| text2           | True             | text2            |

 


tables: name: customer
data: customer.csv
name: DateTable
data: DateTable.csv
name: employee
data: employee.csv
name: location
data: location.csv
name: products
data: products.csv
name: sales
data: sales.csv
name: SampleFilterTable
data: SampleFilterTable.csv
name: SampleTable
data: SampleTable.csv
label: sales
customer_id
customer
customer_id
sales
employee_id
employee
employee_id
sales
product_id
products
product_id
sales
location_id
location
id
output: {
    "joins": [
        {
            "from_table": "sales",
            "from_column": "customer_id",
            "to_table": "customer",
            "to_column": "customer_id"
        },
        {
            "from_table": "sales",
            "from_column": "employee_id",
            "to_table": "employee",
            "to_column": "employee_id"
        },
        {
            "from_table": "sales",
            "from_column": "product_id",
            "to_table": "products",
            "to_column": "product_id"
        },
        {
            "from_table": "sales",
            "from_column": "location_id",
            "to_table": "location",
            "to_column": "id"
        }
    ]
}