Abstract: A method includes receiving, at a processor, first source data structured according to a first schema. The method further includes receiving, at the processor, second source data structured according to a second schema different from the first schema. The method further includes outputting, by the processor, event stream data for storage at a storage device, the event stream data representing a first event extracted from the first source data and a second event extracted from the second source. The event stream data includes a plurality of events corresponding to changes associated with a subject instance. The method also includes generating a profile of the subject instance based on the event stream data. The profile indicates a chronology of the changes associated with the subject instance.

Abstract: A method includes receiving, at a processor, first source data structured according to a first schema. The method further includes receiving, at the processor, second source data structured according to a second schema different from the first schema. The method further includes outputting, by the processor, event stream data for storage at a storage device, the event stream data representing a first event extracted from the first source data and a second event extracted from the second source. The event stream data includes a plurality of events corresponding to changes associated with a subject instance. The method also includes generating a profile of the subject instance based on the event stream data. The profile indicates a chronology of the changes associated with the subject instance.

Abstract: A method includes receiving, at a processor, event stream data, the event stream data including a plurality of events associated with changes to an instance of a subject. The method further includes receiving, at the processor of the computing device, input indicating correction data. The method further includes outputting, from the processor of the computing device, modified event stream data to a storage device. The modified event stream data is generated based on the correction data by modifying an event of the event stream data, adding a first event to the event stream data, deleting a second event from the event stream data, or a combination thereof.

Abstract: A method includes receiving, at a processor, first source data structured according to a first schema. The method further includes receiving, at the processor, second source data structured according to a second schema different from the first schema. The method further includes outputting, by the processor, event stream data for storage at a storage device, the event stream data representing a first event extracted from the first source data and a second event extracted from the second source. The event stream data includes a plurality of events corresponding to changes associated with a subject instance. The method also includes generating a profile of the subject instance based on the event stream data. The profile indicates a chronology of the changes associated with the subject instance.

Abstract: A method includes receiving, at a processor, event stream data, the event stream data including a plurality of events associated with changes to an instance of a subject. The method further includes receiving, at the processor of the computing device, input indicating correction data. The method further includes outputting, from the processor of the computing device, modified event stream data to a storage device. The modified event stream data is generated based on the correction data by modifying an event of the event stream data, adding a first event to the event stream data, deleting a second event from the event stream data, or a combination thereof.

Abstract: A method includes receiving, at a processor, event stream data, the event stream data including a plurality of events associated with changes to an instance of a subject. The method further includes receiving, at the processor of the computing device, input indicating correction data. The method further includes outputting, from the processor of the computing device, modified event stream data to a storage device. The modified event stream data is generated based on the correction data by modifying an event of the event stream data, adding a first event to the event stream data, deleting a second event from the event stream data, or a combination thereof.

Abstract: An analytics platform provides a unified business intelligence application that includes querying, analysis, reporting, and prediction (QARP). The analytics platform includes an analytics engine that stores data identifying a plurality of dimensions associated with a population. The population includes multiple population members and each dimension is associated with multiple dimension members. The data includes likelihood scores for at least a subset of the plurality of dimension members, where the likelihood scores are associated with satisfaction of a criterion. The analytics engine is configured to determine a predicted likelihood of a particular population member satisfying the analysis criterion based on the likelihood scores of the dimension members associated with the population member. The analytics engine may store the predicted likelihood as a calculated value that can be counted, summed, etc. during operation.

Abstract: A method includes receiving, at a server from a computing device, a query identifying a source multidimensional cube, a mapped measure associated with the source multidimensional cube, and one or more classification values. The mapped measure corresponds to a measure associated with a mapped multidimensional cube that is distinct from the source multidimensional cube. The method also includes generating a cell set, wherein each cell of the cell set corresponds to a respective combination of the one or more classification values. The method further includes executing the mapped measure with respect to each cell of the cell set to generate one or more query execution results and sending the one or more query execution results to the computing device.

Abstract: A method includes identifying a source fact table, where the source fact table includes a first set of dimensions and corresponds to a source multidimensional cube that is associated with a first set of measures. The method also includes identifying a mapped multidimensional cube, where the mapped multidimensional cube is associated with a second set of dimensions and a second set of measures. The method further includes adding, to the source fact table, a one or more mapped dimensions corresponding to the second set of dimensions. The method includes associating, with the source multidimensional cube, one or more mapped measures corresponding to the second set of measures.