Abstract: A method for selecting a policy includes evaluating a number of metrics for a number of policies including generating, for each policy of a number of policies, values of the metrics, the generating including applying the policy to a system to generate the values, processing the values of the metrics for the number of policies to determine a boundary of combinations of metric values, combinations on the boundary representing a policy such that no other policy of the number of policies improves all the metric values, presenting, in a user interface, the boundary of combinations of metric values, receiving, at the user interface, a selection of one of the policies made based on the boundary, and providing configuration data associated with the selected policy for implementation in a deployed system.

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.

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.