Abstract: Automated weighting is performed that includes transforming a behavior of each respective dimension of multiple dimensions of a selected group of events to a respective weight, the respective weight determined based on a distribution of values of the respective dimension, and where the weight determined for a first of the plurality of dimensions is greater than the weight determined for a second of the plurality of dimensions. Similarity values are computed indicating similarities between further events and the selected group of events, the similarity values based on a combination of the weights and distances between the further events and the selected group of events. Cohorts of the further events are generated by performing multi-level ranking that comprises ranking groups of the further events based on the similarity values, and applying merging to the groups to produce merged groups. The cohorts are visualized in a graphical visualization.

Abstract: Automated weighting is performed that includes transforming a behavior of each respective dimension of multiple dimensions of a selected group of events to a respective weight, the respective weight determined based on a distribution of values of the respective dimension, and where the weight determined for a first of the plurality of dimensions is greater than the weight determined for a second of the plurality of dimensions. Similarity values are computed indicating similarities between further events and the selected group of events, the similarity values based on a combination of the weights and distances between the further events and the selected group of events. Cohorts of the further events are generated by performing multi-level ranking that comprises ranking groups of the further events based on the similarity values, and applying merging to the groups to produce merged groups. The cohorts are visualized in a graphical visualization.

Abstract: A method and/or computer program product recovers files that are generated by an application running on a client-server system that comprises a back-up client with a client back-up tool and a server with a server back-up tool. Application files are backed up on the server, and then restored to a back-up client based on file usage behavior of the application and their priority, and file stubs are created for remaining files. File usage behavior of the application performing data recovery and regular data processing after said restore process are monitored and analyzed, and files in different types of priority classes are classified based on file usage behavior. Existing file stubs at the back-up client are replaced with corresponding file content from the back-up server during runtime of the application based on predetermined criteria.

Abstract: Techniques are disclosed for determining reasons underlying insights gleaned from multi-dimensional data. In one embodiment, a contingency table is accessed that represents multiple dimensions of the data, in order to identify one or more insights. One or more dimensions, other than the represented dimensions, are evaluated to identify one or more reasons underlying a first insight of the one or more insights, and the one or more reasons are output.

Abstract: Techniques are disclosed for determining reasons underlying insights gleaned from multi-dimensional data. In one embodiment, a contingency table is accessed that represents multiple dimensions of the data, in order to identify one or more insights. One or more dimensions, other than the represented dimensions, are evaluated to identify one or more reasons underlying a first insight of the one or more insights, and the one or more reasons are output.

Abstract: A method and/or computer program product recovers files that are generated by an application running on a client-server system that comprises a back-up client with a client back-up tool and a server with a server back-up tool. Application files are backed up on the server, and then restored to a back-up client based on file usage behavior of the application and their priority, and file stubs are created for remaining files. File usage behavior of the application performing data recovery and regular data processing after said restore process are monitored and analyzed, and files in different types of priority classes are classified based on file usage behavior. Existing file stubs at the back-up client are replaced with corresponding file content from the back-up server during runtime of the application based on predetermined criteria.

Abstract: Techniques are disclosed for determining reasons underlying insights gleaned from multi-dimensional data. In one embodiment, a contingency table is accessed that represents multiple dimensions of the data, in order to identify one or more insights. One or more dimensions, other than the represented dimensions, are evaluated to identify one or more reasons underlying a first insight of the one or more insights, and the one or more reasons are output.

Abstract: Techniques are disclosed for determining reasons underlying insights gleaned from multi-dimensional data. In one embodiment, a contingency table is accessed that represents multiple dimensions of the data, in order to identify one or more insights. One or more dimensions, other than the represented dimensions, are evaluated to identify one or more reasons underlying a first insight of the one or more insights, and the one or more reasons are output.