Detection and utilization of similarities among tables in different data systems

A computer program product, comprising one or more computer readable hardware storage devices having computer readable program code stored therein, said program code containing instructions executable by one or more processors of a computer system to implement a method for detection and utilization of similarities among tables in a plurality of data systems that include a first data system and a second data system, each data system being a different data system that comprises a plurality of tables, each table in each data system comprising a table title and a plurality of columns and multiple rows comprising a header row and a plurality of data rows, each header row comprising a column name for each column, each table title and each column name having semantic metadata comprising text, said method comprising:

• generating, by one or more processors, a semantic dataset comprising a set of keywords and a set of binary values of 1 or 0 denoting a presence or absence, respectively, of each keyword in the semantic metadata in the column names, each keyword in the set of keywords being a unique keyword within the set of keywords;
• computing, by the one or more processors using the semantic dataset in conjunction with the semantic metadata in the column names, a first measure (X) of similarity between the columns in the tables in the first data system and the columns in the tables in the second data system;
• computing, by the one or more processors using the semantic dataset in conjunction with the semantic metadata in the column names, a second measure (Y) of similarity between the columns in the tables in the first data system and the columns in the tables in the second data system, said first measure X and said second measure Y being different measures of similarity;
• computing, by the one or more processors based on data in cells within columns in the tables in the both the first data system and the second data system, a third measure (Z) of similarity between the columns in the tables in the first data system and the columns of in the tables in the second data system;
• computing, by the one or more processors, a weighted combination (U) of X, Y, and Z between the columns in the tables in the first data system and the columns in the tables in the second data system;
• and
• using, by the one or more processors, one or more computed parameters selected from the group consisting of X, Y, Z, U and combinations thereof to implement a practical application that improves the computer system, wherein said using comprises using X, Y, and Z, and wherein X>0.90, Y>0.90, and Z>0.90 in at least 95% of combinations of a column in a first table in the first data system and a column in a second table in the second data system.

A computer system, comprising one or more processors, one or more memories, and one or more computer readable hardware storage devices, said one or more hardware storage devices containing program code executable by the one or more processors via the one or more memories to implement a method for detection and utilization of similarities among tables in a plurality of data systems that include a first data system and a second data system, each data system being a different data system that comprises a plurality of tables, each table in each data system comprising a table title and a plurality of columns and multiple rows comprising a header row and a plurality of data rows, each header row comprising a column name for each column, each table title and each column name having semantic metadata comprising text, said method comprising:

• generating, by one or more processors, a semantic dataset comprising a set of keywords and a set of binary values of 1 or 0 denoting a presence or absence, respectively, of each keyword in the semantic metadata in the column names, each keyword in the set of keywords being a unique keyword within the set of keywords;
• computing, by the one or more processors using the semantic dataset in conjunction with the semantic metadata in the column names, a first measure (X) of similarity between the columns in the tables in the first data system and the columns in the tables in the second data system;
• computing, by the one or more processors using the semantic dataset in conjunction with the semantic metadata in the column names, a second measure (Y) of similarity between the columns in the tables in the first data system and the columns in the tables in the second data system, said first measure X and said second measure Y being different measures of similarity;
• computing, by the one or more processors based on data in cells within columns in the tables in the both the first data system and the second data system, a third measure (Z) of similarity between the columns in the tables in the first data system and the columns of in the tables in the second data system;
• computing, by the one or more processors, a weighted combination (U) of X, Y, and Z between the columns in the tables in the first data system and the columns in the tables in the second data system;
• and
• using, by the one or more processors, one or more computed parameters selected from the group consisting of X, Y, Z, U and combinations thereof to implement a practical application that improves the computer system, wherein said using comprises using X, Y, and Z, and wherein X>0.90, Y>0.90, and Z>0.90 in at least 95% of combinations of a column in a first table in the first data system and a column in a second table in the second data system a first table in the first data system and an other column in a second table in the second data system.