Abstract: The invention provides a method and system to simulate a process of discrete tasks having a plurality of available resources associated therewith. The system may comprise a model database to store a plurality of models, each model including a plurality of task and resource parameters; a model portion in communication with the model database and configured to receive commands from a user, to retrieve one of the plurality of models and corresponding task and resource parameters in response to a user command, to receive input data corresponding to attributes of one or more task and resource parameters from a business database system, and to generate a simulation model based on the selected business database system and the input data; and a model server to perform a simulation of the process by processing the simulation model and to generate an output data file containing output data representative of the simulation.

Abstract: A system for detecting potentially adverse conditions includes a plurality of different types of sensors each adapted to monitor a different measured parameter of an infrastructure. The system also includes a model that fuses data from the plurality of sensors and provides an indication of a potentially adverse condition. A method is also provided for detecting potentially adverse conditions.

Abstract: Methods, systems, and computer program products for identifying assets for resource allocation are provided. The method includes gathering data that includes asset data and ambient area data. The asset data relates to each asset in an infrastructure and the ambient area data relates to a geographic region surrounding a corresponding asset. The method also includes analyzing the asset and ambient area data to determine the propensity for an event or condition, occurring within each ambient area, to have an unfavorable impact on the asset. The method further includes rating a severity of the unfavorable impact, and selecting at least one asset in the infrastructure for allocating a resource based upon the rating.

Abstract: A visualization technique for directing the attention of analysts to anomalous values of performance measures associated with a target entity is described. A grid of cells is created where each row represents a particular performance metric, and each column a particular time period. For each cell, an anomaly score is calculated associated with the performance metric and time period corresponding to the row and column of the cell. The anomaly score is based on the value of the performance metric for that particular entity for that time period, as well as context data. The context data is selected to represent the historical values of the performance metric for the target entity or the simultaneous performance of peer entities. The anomaly score is calculated using an exceptional statistical technique, and a display characteristic is associated with the value of the anomaly score based upon the range into which the anomaly score falls.