Abstract: Exemplary embodiments relate to systems for building a model of changes to data items when information the data items is limited or not directly observed. Exemplary embodiments allow properties of the data items to be inferred using a single data structure and creates a highly granular log of changes to the data item. Using this data structure, the time-varying nature of changes to the data item can be determined. The data structure may be used to identify characteristics associated with a regularly-performed action, to examine how adherence to the action affects a system, and to identify outcomes of non-adherence. Fungible data items may be mapped to a remediable condition or remedy class. This may be accomplished by automatically deriving conditions and remedial information from available information, matching the conditions to remedial classes or types via a customizable mapping, and then calculating adherence for the condition on the available information.

Abstract: Exemplary embodiments relate to systems for building a model of changes to data items when information the data items is limited or not directly observed. Exemplary embodiments allow properties of the data items to be inferred using a single data structure and creates a highly granular log of changes to the data item. Using this data structure, the time-varying nature of changes to the data item can be determined. The data structure may be used to identify characteristics associated with a regularly-performed action, to examine how adherence to the action affects a system, and to identify outcomes of non-adherence. Fungible data items may be mapped to a remediable condition or remedy class. This may be accomplished by automatically deriving conditions and remedial information from available information, matching the conditions to remedial classes or types via a customizable mapping, and then calculating adherence for the condition on the available information.

Abstract: Exemplary embodiments relate to systems for building a model of changes to data items when information the data items is limited or not directly observed. Exemplary embodiments allow properties of the data items to be inferred using a single data structure and creates a highly granular log of changes to the data item. Using this data structure, the time-varying nature of changes to the data item can be determined. The data structure may be used to identify characteristics associated with a regularly-performed action, to examine how adherence to the action affects a system, and to identify outcomes of non-adherence. Fungible data items may be mapped to a remediable condition or remedy class. This may be accomplished by automatically deriving conditions and remedial information from available information, matching the conditions to remedial classes or types via a customizable mapping, and then calculating adherence for the condition on the available information.

Abstract: Exemplary embodiments relate to systems for building a model of changes to data items when information the data items is limited or not directly observed. Exemplary embodiments allow properties of the data items to be inferred using a single data structure and creates a highly granular log of changes to the data item. Using this data structure, the time-varying nature of changes to the data item can be determined. The data structure may be used to identify characteristics associated with a regularly-performed action, to examine how adherence to the action affects a system, and to identify outcomes of non-adherence. Fungible data items may be mapped to a remediable condition or remedy class. This may be accomplished by automatically deriving conditions and remedial information from available information, matching the conditions to remedial classes or types via a customizable mapping, and then calculating adherence for the condition on the available information.

Abstract: One or more embodiments may include techniques to identify anomalies based on computer-generated results. Moreover, embodiments may include applying scenario rules to data to detect scenario violations and grouping the scenario violations into scenario clusters based on similar behavior performed by entities indicated by similarity metrics. embodiments include determining predictive ability values for each of the scenario clusters, ranking the scenario clusters based on the predictive ability values, and removing scenario clusters having predictive ability values below a threshold. In embodiments combinations of scenario clusters may be generated from the set of scenario clusters and the combinations of scenario clusters may be evaluated for effectiveness. Embodiments include generating scores for entities of the combinations of scenario clusters deemed effective, and provide results indicating whether one or more of the entities committed an anomaly based on the scores for each of the entities.

Abstract: One or more embodiments may include techniques to identify anomalies based on computer-generated results. Moreover, embodiments may include applying scenario rules to data to detect scenario violations and grouping the scenario violations into scenario clusters based on similar behavior performed by entities indicated by similarity metrics. embodiments include determining predictive ability values for each of the scenario clusters, ranking the scenario clusters based on the predictive ability values, and removing scenario clusters having predictive ability values below a threshold. In embodiments combinations of scenario clusters may be generated from the set of scenario clusters and the combinations of scenario clusters may be evaluated for effectiveness. Embodiments include generating scores for entities of the combinations of scenario clusters deemed effective, and provide results indicating whether one or more of the entities committed an anomaly based on the scores for each of the entities.