Abstract: Described are a system, method, and computer program product for reconfiguring a data table for processing on a server cluster. The method includes extracting a data table from a relational database and determining whether the data table includes a column having a range of values with a uniform distribution. The method also includes, in response to determining that the data table includes the column, classifying the column as a candidate column for splitting the data table. The method further includes, in response to determining that the data table does not include the column, inserting an index column into the data table and classifying the index column as the candidate column. The method further includes splitting the data table based on the candidate column and distributing each subdivision to a node of the server cluster so as to cause the server cluster to collectively process the data table.

Abstract: Described are a system, method, and computer program product for reconfiguring a data table for processing on a server cluster. The method includes extracting a data table from a relational database and determining whether the data table includes a column having a range of values with a uniform distribution. The method also includes, in response to determining that the data table includes the column, classifying the column as a candidate column for splitting the data table. The method further includes, in response to determining that the data table does not include the column, inserting an index column into the data table and classifying the index column as the candidate column. The method further includes splitting the data table based on the candidate column and distributing each subdivision to a node of the server cluster so as to cause the server cluster to collectively process the data table.

Abstract: A method, system, and computer program product for co-located merchant anomaly detection obtain prior transaction data associated with a plurality of prior transactions, a first subset being associated with a first merchant, a second subset being associated with a second merchant, each prior transaction being associated with a same merchant location in the prior transaction data, and each prior transaction being associated with a same merchant name in the prior transaction data; extract a plurality of features associated with the plurality of prior transactions from the prior transaction data; and train based on the plurality of features associated with the plurality of prior transactions and labels for the prior transactions, a machine learning model to determine at least one of a prediction of whether a transaction is associated with the first merchant and a prediction of whether the transaction is associated with the second merchant.