Abstract: A computer system processes a plurality of unrelated database tables to create a join graph where each node in the join graph represents a table. The nodes in the join graph are connected by weighted, directed edges, where each directed edge represents a join from a first column in a first table to a second column in a second table and where the weight of the directed edge represents a predicted level of success in performing the join. The edge weights can be based on a likelihood of finding a value from the first column in the second column. A user selects a subset of the tables, and the system creates a join tree with recommended joins between the tables selected by the user. The recommended joins are used to create a structured query language statement which is executed to return a result to the user.

Abstract: A computer system processes a plurality of unrelated database tables to create a join graph where each node in the join graph represents a table. The nodes in the join graph are connected by weighted, directed edges, where each directed edge represents a join from a first column in a first table to a second column in a second table and where the weight of the directed edge represents a predicted level of success in performing the join. The edge weights can be based on a likelihood of finding a value from the first column in the second column. A user selects a subset of the tables, and the system creates a join tree with recommended joins between the tables selected by the user. The recommended joins are used to create a structured query language statement which is executed to return a result to the user.

Abstract: A computer system processes a plurality of unrelated database tables to create a join graph where each node in the join graph represents a table. The nodes in the join graph are connected by weighted, directed edges, where each directed edge represents a join from a first column in a first table to a second column in a second table and where the weight of the directed edge represents a predicted level of success in performing the join. The edge weights can be based on a likelihood of finding a value from the first column in the second column. A user selects a subset of the tables, and the system creates a join tree with recommended joins between the tables selected by the user. The recommended joins are used to create a structured query language statement which is executed to return a result to the user.

Abstract: A computer system processes a plurality of unrelated database tables to create a join graph where each node in the join graph represents a table. The nodes in the join graph are connected by weighted, directed edges, where each directed edge represents a join from a first column in a first table to a second column in a second table and where the weight of the directed edge represents a predicted level of success in performing the join. The edge weights can be based on a likelihood of finding a value from the first column in the second column. A user selects a subset of the tables, and the system creates a join tree with recommended joins between the tables selected by the user. The recommended joins are used to create a structured query language statement which is executed to return a result to the user.

Abstract: A computer system processes arbitrary data sets to identify fields of data that can be the basis of a join operation. Each data set has a plurality of entries, with each entry having a plurality of fields. For each pair of data sets, the computer system compares the values of fields in a first data set in the pair of data sets to the values of fields in a second data set in the pair of data sets, to identify fields having substantially similar sets of values. Given pairs of fields that have similar sets of values, the computer system measures entropy with respect to an intersection of the sets of values of the pair of fields. The computer system can recommend fields for a join operation between any pair of data sets in the plurality of data sets based on such statistical measures.

Abstract: A system and method for parallelizing applications of script-driven software tools. Scripts in the software tool scripting language are automatically analyzed in order to produce a specification for a parallel computation plus a set of “script fragments”, the combination of which is functionally equivalent to the original script. The computational specification plus the script fragments are then executed by a parallel runtime system, which causes multiple instances of the original software tool and/or supplemental programs to be run as parallel processes. The resulting processes will read input data and produce output data, performing the same computation as was specified by the original script.