Abstract: A device may communicate with a server to obtain historical ticket data. The device may generate a data model, based on the historical ticket data. The device may communicate with a client device to obtain ticket data relating to an issue associated with a project. The device may classify, using the data model, the ticket data into a ticket type. The device may generate, using the data model and based on the ticket type, a set of recommended resolutions for resolving the issue associated with the project. The device may select, from the set of recommended resolutions, a particular resolution based on a set of selection criteria. The device may automatically perform one or more actions to implement the particular resolution to resolve the issue associated with the project.

Abstract: A device may communicate with a server to obtain historical ticket data. The device may generate a data model, based on the historical ticket data. The device may communicate with a client device to obtain ticket data relating to an issue associated with a project. The device may classify, using the data model, the ticket data into a ticket type. The device may generate, using the data model and based on the ticket type, a set of recommended resolutions for resolving the issue associated with the project. The device may select, from the set of recommended resolutions, a particular resolution based on a set of selection criteria. The device may automatically perform one or more actions to implement the particular resolution to resolve the issue associated with the project.

Abstract: A device may communicate with a server to obtain historical ticket data. The device may generate a data model, based on the historical ticket data. The device may communicate with a client device to obtain ticket data relating to an issue associated with a project. The device may classify, using the data model, the ticket data into a ticket type. The device may generate, using the data model and based on the ticket type, a set of recommended resolutions for resolving the issue associated with the project. The device may select, from the set of recommended resolutions, a particular resolution based on a set of selection criteria. The device may automatically perform one or more actions to implement the particular resolution to resolve the issue associated with the project.

Abstract: A device may communicate with a server to obtain historical ticket data. The device may generate a data model, based on the historical ticket data. The device may communicate with a client device to obtain ticket data relating to an issue associated with a project. The device may classify, using the data model, the ticket data into a ticket type. The device may generate, using the data model and based on the ticket type, a set of recommended resolutions for resolving the issue associated with the project. The device may select, from the set of recommended resolutions, a particular resolution based on a set of selection criteria. The device may automatically perform one or more actions to implement the particular resolution to resolve the issue associated with the project.

Abstract: A multi-range data set graph control and adjustment process. A multi-range data set graph adjustment control can include a graph display having at least two axises. At least one of the axises can include a contiguous set of selectable and adjustable segments. A rescaling processor can be configured to rescale segments which are adjacent to an adjusted segment in a proportional manner according to the distance of each of the adjacent segments from the adjusted segment. Also, a graph display plotter can be configured to plot graphs of data in the multi-range data set according the scale of each segment in the contiguous set of selectable and adjustable segments. Notably, the control can be included in an application server analysis tool to provide a visual display of multi-range data sets produced by the analysis tool.

Abstract: A method, apparatus and computer instructions are provided to improve the push/pull workload management model with intelligent routing to effectively collect data from systems that consist of dynamic sub-systems. The invention improves the push/pull model of the referenced invention with intelligent request routing to solve the above problem. An API is exposed in the workload manager enabling its request router to decline requests to route to idle sub-systems. This allows the monitoring agent to avoid sending a pull request to an idle sub-system. Each sub-system will push the data to the agent as it enters the idle state. The agent caches the data it receives from idle sub-systems and combines it with data it pulls from active and stopped sub-systems.

Abstract: A multi-range data set graph control and adjustment process. A multi-range data set graph adjustment control can include a graph display having at least two axises. At least one of the axises can include a contiguous set of selectable and adjustable segments. A rescaling processor can be configured to rescale segments which are adjacent to an adjusted segment in a proportional manner according to the distance of each of the adjacent segments from the adjusted segment. Also, a graph display plotter can be configured to plot graphs of data in the multi-range data set according the scale of each segment in the contiguous set of selectable and adjustable segments. Notably, the control can be included in an application server analysis tool to provide a visual display of multi-range data sets produced by the analysis tool.