In this stage, data should have common variable names and same number of entities.
Question come to how to identify that whether data 1 is a shuffled row version of data 2.
For now, for simplicity, we could assume that we have unique identifiers for each data set that is one to one mapping to row number for corresponding data. In this case, we could define a hashing map in data 1 : row number to unique identifiers. Then use it to match the unique identifiers in data 2 and then compare the result of hashing map with [1, ..., n] to see if they are the same.
If the data does not have common unique identifiers, record linkage model could be applied here. [TO DO]
In this stage, data should have common variable names and same number of entities.
Question come to how to identify that whether data 1 is a shuffled row version of data 2.
For now, for simplicity, we could assume that we have unique identifiers for each data set that is one to one mapping to row number for corresponding data. In this case, we could define a hashing map in data 1 : row number to unique identifiers. Then use it to match the unique identifiers in data 2 and then compare the result of hashing map with [1, ..., n] to see if they are the same.
If the data does not have common unique identifiers, record linkage model could be applied here. [TO DO]