Abstract: Methods and apparatus teach providing semantic associations between data available on one or more computing devices, including grouping together related files and creating an association between the related grouped files and at least one anchor file to provide a semantic association for the grouped files. Also is taught configuring an agent on the one or more computing devices to undertake the grouping and to create the association without a user request. Also is taught triggering an evaluation of current files against related grouped files, and creating an association between the current files and at least one of the related grouped files and the at least one anchor file. Information may be added to the created association to create additional semantic associations for one or more of the grouped files and the current files. In turn, computer program products and computing systems for accomplishing the foregoing are provided.

Abstract: A machine receives a description of the relationships among members of a data set. The machine constructs a graph that represents the relationships among the members of the data set, organizing the members of the data set into groups. The groups are analyzed to determine their relative strengths. Unbalanced groups can be balanced by splitting off heavy sub-trees that include too large a percentage of the nodes in the group. The machine can then use the graph to answer queries about members of the data set.

Abstract: A machine receives a group of members of a data set. The machine identifies key symbols from the members of the group or the data set. The machine then calculates, for each key symbol, a weighted magnitude for the key symbol in the group. The machine can then sort the key symbols according to their weighted magnitudes, and filter out common key symbols. The uncommon key symbols, as sorted according to their weighted magnitudes, can form a name for the group.

Abstract: A machine receives a description of the relationships among members of a data set. The machine constructs a graph that represents the relationships among the members of the data set, organizing the members of the data set into groups. The groups are analyzed to determine their relative strengths. Unbalanced groups can be balanced by splitting off heavy sub-trees that include too large a percentage of the nodes in the group. The machine can then use the graph to answer queries about members of the data set.

Abstract: A machine receives a description of the relationships among members of a data set. The machine constructs a graph that represents the relationships among the members of the data set, organizing the members of the data set into groups. The groups are analyzed to determine their relative strengths. Unbalanced groups can be balanced by splitting off heavy sub-trees that include too large a percentage of the nodes in the group. The machine can then use the graph to answer queries about members of the data set.

Abstract: Methods and apparatus filter out unused information in irrelevant patterns to find an optimized relevancy group key. Such an optimized key occupies a smaller mapping space and functions to identify relevancy groups while requiring fewer computations to perform thereby improving the overall speed and performance of the processing device.

Abstract: A machine receives a description of the relationships among members of a data set. The machine constructs a graph that represents the relationships among the members of the data set, organizing the members of the data set into groups. The groups are analyzed to determine their relative strengths. Unbalanced groups can be balanced by splitting off heavy sub-trees that include too large a percentage of the nodes in the group. The machine can then use the graph to answer queries about members of the data set.

Abstract: A machine receives a group of members of a data set. The machine identifies key symbols from the members of the group or the data set. The machine then calculates, for each key symbol, a weighted magnitude for the key symbol in the group. The machine can then sort the key symbols according to their weighted magnitudes, and filter out common key symbols. The uncommon key symbols, as sorted according to their weighted magnitudes, can form a name for the group.

Abstract: Methods and computer program product relate to the animation of data. The methods and product are executable on a processing device in a computing system environment so as to effectively empower new data files to find and make associations with other applications or principles that already have built up associations or binding with data files that are similar to the new data file.

Abstract: Methods and apparatus teach providing semantic associations between data available on one or more computing devices, including grouping together related files and creating an association between the related grouped files and at least one anchor file to provide a semantic association for the grouped files. Also is taught configuring an agent on the one or more computing devices to undertake the grouping and to create the association without a user request. Also is taught triggering an evaluation of current files against related grouped files, and creating an association between the current files and at least one of the related grouped files and the at least one anchor file. Information may be added to the created association to create additional semantic associations for one or more of the grouped files and the current files. In turn, computer program products and computing systems for accomplishing the foregoing are provided.