Abstract: A method of bootstrap sampling a dataset is described. With a process node, a series of random integers is generated. An assignment map is created. The assignment map includes a row identifier for each row of data of the dataset. A plurality of bootstrap sample identifiers defined by the series are assigned to at least one row identifier. An output table created from the assignment map. Rows of the output table include each instance of the bootstrap sample identifiers, the row identifier assigned with the bootstrap sample identifier, and data of the row.

Abstract: A method of bootstrap sampling a dataset is described. With a process node, a series of random integers is generated. An assignment map is created. The assignment map includes a row identifier for each row of data of the dataset. A plurality of bootstrap sample identifiers defined by the series are assigned to at least one row identifier. An output table created from the assignment map. Rows of the output table include each instance of the bootstrap sample identifiers, the row identifier assigned with the bootstrap sample identifier, and data of the row.

Abstract: A method of bootstrap sampling a dataset is described. With a process node, a series of random integers is generated. An assignment map is created. The assignment map includes a row identifier for each row of data of the dataset. A plurality of bootstrap sample identifiers defined by the series are assigned to at least one row identifier. An output table created from the assignment map. Rows of the output table include each instance of the bootstrap sample identifiers, the row identifier assigned with the bootstrap sample identifier, and data of the row.

Abstract: A method of bootstrap sampling a dataset is described. With a process node, a series of random integers is generated. An assignment map is created. The assignment map includes a row identifier for each row of data of the dataset. A plurality of bootstrap sample identifiers defined by the series are assigned to at least one row identifier. An output table created from the assignment map. Rows of the output table include each instance of the bootstrap sample identifiers, the row identifier assigned with the bootstrap sample identifier, and data of the row.

Abstract: A method of bootstrap sampling a dataset is described. With a process node, a series of random integers is generated. An assignment map is created. The assignment map includes a row identifier for each row of data of the dataset. A plurality of bootstrap sample identifiers defined by the series are assigned to at least one row identifier. An output table created from the assignment map. Rows of the output table include each instance of the bootstrap sample identifiers, the row identifier assigned with the bootstrap sample identifier, and data of the row.

Abstract: A method of bootstrap sampling a dataset is described. With a process node, a series of random integers is generated. An assignment map is created. The assignment map includes a row identifier for each row of data of the dataset. A plurality of bootstrap sample identifiers defined by the series are assigned to at least one row identifier. An output table created from the assignment map. Rows of the output table include each instance of the bootstrap sample identifiers, the row identifier assigned with the bootstrap sample identifier, and data of the row.

Abstract: A method of bootstrap sampling a dataset is described. With a process node, a series of random integers is generated. An assignment map is created. The assignment map includes a row identifier for each row of data of the dataset. A plurality of bootstrap sample identifiers defined by the series are assigned to at least one row identifier. An output table created from the assignment map. Rows of the output table include each instance of the bootstrap sample identifiers, the row identifier assigned with the bootstrap sample identifier, and data of the row.