Abstract: A method, computer program product, and computer system for detection and utilization of similarities among tables in multiple data systems that include a first data system and a second data system. A semantic dataset is generated. A first measure (X) of similarity between semantic data in columns of the first and second data systems is computed using the semantic dataset. A first, different measure (Y) of similarity between semantic data in columns of the first and second data systems is computed using the semantic dataset. A third measure (Z) similarity between columns of the first and second data systems is computed based on data in cells within the columns. A weighted combination (U) of X, Y, and Z between the columns of tables in the first and second data systems is computed. X, Y, Z, U or combinations thereof are used to improve a computer system.

Abstract: A method, computer program product, and computer system for detection and utilization of similarities among tables in multiple data systems that include a first data system and a second data system. A semantic dataset is generated. A first measure (X) of similarity between semantic data in columns of the first and second data systems is computed using the semantic dataset. A first, different measure (Y) of similarity between semantic data in columns of the first and second data systems is computed using the semantic dataset. A third measure (Z) similarity between columns of the first and second data systems is computed based on data in cells within the columns. A weighted combination (U) of X, Y, and Z between the columns of tables in the first and second data systems is computed. X, Y, Z, U or combinations thereof are used to improve a computer system.

Abstract: A computer-implemented method comprises analyzing content sections on each of a plurality of open browser pages using natural language processing to identify one or more topics on each of the plurality of open browser pages; calculating a respective relevance score for each of the content sections; grouping each of the plurality of topics into one of a plurality of topic groups; calculating a respective group ranking for each of the plurality of topic groups based on the respective relevance score for each content section. The method further comprises, for each topic group, assigning the respective group ranking to all of the content sections corresponding to the respective topic group; and, for each of the plurality of open browser pages, selecting at least one content section having a highest group ranking and modifying a display of the respective open browser page to direct attention to the selected content section.

Abstract: A computer-implemented method comprises analyzing content sections on each of a plurality of open browser pages using natural language processing to identify one or more topics on each of the plurality of open browser pages; calculating a respective relevance score for each of the content sections; grouping each of the plurality of topics into one of a plurality of topic groups; calculating a respective group ranking for each of the plurality of topic groups based on the respective relevance score for each content section. The method further comprises, for each topic group, assigning the respective group ranking to all of the content sections corresponding to the respective topic group; and, for each of the plurality of open browser pages, selecting at least one content section having a highest group ranking and modifying a display of the respective open browser page to direct attention to the selected content section.