Abstract: The present disclosure involves systems, software, and computer implemented methods for proactively predicting demand based on sparse transaction data. One example method includes receiving a request to predict transaction quantities for a plurality of transaction entities for a future time period. Historical transaction data for the transaction entities is identified for a plurality of categories of transacted items. The plurality of categories are organized using a hierarchy of levels. Multiple levels of the hierarchy are iterated over starting at a lowest level. For each current level in the iteration, features to include in a quantity forecasting model for the current level are identified. The quantity forecasting model is trained using the identified features. Predicted transaction dates are predicted for the current level by a transaction date prediction model. The quantity forecasting model is used to generate predicted quantity information for the current level for the predicted transaction dates.

Abstract: The present disclosure involves systems, software, and computer implemented methods for proactively predicting demand based on sparse transaction data. One example method includes receiving a request to predict transaction dates for a plurality of transaction entities for a future time period. Historical transaction data for the transaction entities is identified for a plurality of categories of transacted items. The plurality of categories are organized using a hierarchy of levels. Multiple levels of the hierarchy are iterated over, starting at a lowest level. For each current level in the iteration, a plurality of transaction date prediction models are trained and tested. Heuristics for the plurality of trained transaction date prediction models are compared to determine a most accurate transaction date prediction model. The most accurate transaction date prediction model is used to make a prediction of transaction dates for the current level for the future time period.

Abstract: The present disclosure involves systems, software, and computer implemented methods for proactively predicting demand based on sparse transaction data. One example method includes receiving a request to predict transaction quantities for a plurality of transaction entities for a future time period. Historical transaction data for the transaction entities is identified for a plurality of categories of transacted items. The plurality of categories are organized using a hierarchy of levels. Multiple levels of the hierarchy are iterated over starting at a lowest level. For each current level in the iteration, features to include in a quantity forecasting model for the current level are identified. The quantity forecasting model is trained using the identified features. Predicted transaction dates are predicted for the current level by a transaction date prediction model. The quantity forecasting model is used to generate predicted quantity information for the current level for the predicted transaction dates.

Abstract: The present disclosure involves systems, software, and computer implemented methods for proactively predicting demand based on sparse transaction data. One example method includes receiving a request to predict transaction dates for a plurality of transaction entities for a future time period. Historical transaction data for the transaction entities is identified for a plurality of categories of transacted items. The plurality of categories are organized using a hierarchy of levels. Multiple levels of the hierarchy are iterated over, starting at a lowest level. For each current level in the iteration, a plurality of transaction date prediction models are trained and tested. Heuristics for the plurality of trained transaction date prediction models are compared to determine a most accurate transaction date prediction model. The most accurate transaction date prediction model is used to make a prediction of transaction dates for the current level for the future time period.

Abstract: Disclosed are apparatuses and methods for use of a mesh screen having a perimeter, an elastomeric material, and a flexible frame that surrounds elastomeric material and is attached to the mesh screen at or substantially proximate to the perimeter, wherein the flexible frame surrounds an entirety of the perimeter. In another embodiment, the flexible frame constitutes connected portions of flexible frame having elastomeric material(s) and portions of flexible frame not having elastomeric material(s). Example use may include pushing the apparatus into a window seal of a window opening to prevent macroscopic materials, such as particulates and insets, from ingress or egress into an otherwise closed system, such as the interior of an automobile.

Abstract: The present invention extends to methods, systems, and computer program products for linking diagnostic visualizations to regions of application code. Diagnostic visualizations emitted during execution of an application are displayed. The diagnostic visualizations partially represent the abstract objective of the application (e.g., as envisioned by a developer). Diagnostic data for at least one of a plurality of components is displayed. The diagnostic data indicates the performance of the at least one of the plurality of components during execution of the application. The displayed one or more diagnostic visualizations and the displayed diagnostic data is correlated to link the one or more diagnostic visualizations to the at least one of the plurality of components. Linking the one or more diagnostic visualizations to the at least one of the plurality of components can better indicate how the application's behavior reconciles the abstract objective.

Abstract: The present invention extends to methods, systems, and computer program products for time-based navigation within resource utilization data. A computer system is configured to present resource utilization data representing performance of computer resources. The resource utilization data is displayed on a diagnostic data trace during the execution of the application. The user can select a desired time range and the resource utilization data within the time range will be displayed at other traces. The diagnostic data trace is still presented so that the user can understand the relation between the selected time range and the overall time length. Further, the user can modify the selected time range by change the extents of the selected time range using resizing tool. The resource utilization data within the modified selected time range, similarly, is also displayed along with the diagnostic data trace.

Abstract: The present invention extends to methods, systems, and computer program products for linking diagnostic visualizations to regions of application code. Diagnostic visualizations emitted during execution of an application are displayed. The diagnostic visualizations partially represent the abstract objective of the application (e.g., as envisioned by a developer). Diagnostic data for at least one of a plurality of components is displayed. The diagnostic data indicates the performance of the at least one of the plurality of components during execution of the application. The displayed one or more diagnostic visualizations and the displayed diagnostic data is correlated to link the one or more diagnostic visualizations to the at least one of the plurality of components. Linking the one or more diagnostic visualizations to the at least one of the plurality of components can better indicate how the application's behavior reconciles the abstract objective.

Abstract: The present invention extends to methods, systems, and computer program products for time-based navigation within resource utilization data. A computer system is configured to present resource utilization data representing performance of computer resources. The resource utilization data is displayed on a diagnostic data trace during the execution of the application. The user can select a desired time range and the resource utilization data within the time range will be displayed at other traces. The diagnostic data trace is still presented so that the user can understand the relation between the selected time range and the overall time length. Further, the user can modify the selected time range by change the extents of the selected time range using resizing tool. The resource utilization data within the modified selected time range, similarly, is also displayed along with the diagnostic data trace.