Abstract: A computer-implemented method includes receiving a first dataset including a first table having a first number of parts, where each part of the first number of parts represents either a row or a column of the first table and receiving a second dataset including a second table having a second number of parts, where each part of the second number of parts represents either a row or a column of the second table. For each part of the first number of parts the method includes forming a string representation of the part from a number of values associated with the part and determining a numerical representation of the string representation of the part. For each part of the second number of parts, the method includes forming a string representation of the part from a number of values associated with the part and determining a numerical representation of the string representation of the part.

Abstract: A computer-implemented method comprising: receiving, with a computer, first and second datasets; performing, with the computer, column discovery on the first and second datasets using a first trained machine-learning model to produce a column map that indexes one or more columns in the first dataset to one or more columns in the second dataset; performing, with the computer, row discovery on the first and second datasets using a second trained machine-learning model, a trained approximate nearest neighbor index, and the column discovery to produce a row map that indexes one or more rows in the first dataset to one or more rows in the second dataset; combining, with the computer, the first and second datasets using the column map and the row map to form a combined dataset; and performing one or more actions with the combined dataset.