Abstract: A method includes receiving input identifying a dimensional framework and forecasting time period. The method also includes generating a workforce plan based on the dimensional framework and the forecasting time period. The method further includes receiving first data defining a first scenario of the workforce plan, where the first scenario is associated with at least a first turnover assumption, and determining a first set of forecasted headcount values corresponding to the first scenario. The method includes receiving second data defining a second scenario of the workforce plan, where the second scenario is associated with at least a second turnover assumption that differs from the first turnover assumption, and determining a second set of forecasted headcount values corresponding to the second scenario. The method includes generating graphical user interface (GUI) data that includes a comparison of the first set of forecasted headcount values and the second set of forecasted headcount values.

Abstract: An example method comprises: receiving a natural language text; transforming, by a neural network, the natural language text into a numeric representation comprising a plurality of numeric values; discarding the natural language text; and performing, using the numeric representation of the natural language text, an information extraction task.

Abstract: An example method for gathering a plurality of data sets for a particular process is provided. Each data set indicates transitions between different stages for a corresponding occurrence of the particular process. The method includes generating stage transition data based on the plurality of data sets. The stage transition data indicates an aggregate value for each distinct transition. The method includes determining a root stage based on the plurality of data sets. The method includes selecting each additional stage in the pipeline of stages. Each additional stage is sequentially selected based on a dynamically determined path constructed to reduce a value of a cost function. The method includes selectively modifying the pipeline of stages responsive to detecting an improvement to the value of the cost function. The method also includes generating a command to perform the particular process using the modified pipeline of stages.

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 generating a workforce plan including forecasted headcount values associated with a plurality of dimension members of a plurality of dimensions and receiving input to create a first dimension member of a particular dimension of the plurality of dimensions. The particular dimension has an associated dimension level in a hierarchy corresponding to the particular dimension. The method includes storing data associated with the first dimension member to a data store associated with the workforce plan and receiving headcount values associated with a second dimension member that differs from the first dimension member. The method includes generating a graphical user interface (GUI) indicating that the first dimension member is determined to be a potential match for the second dimension member based on the first dimension member being a member of the particular dimension and having the same dimension level as the second dimension member.

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: Systems and methods of performing comparisons of client data and benchmark data are described. When client data is compared to internal benchmark data, comparative result values may be computed by performing a multidimensional operation with respect to client data for a particular dimension member and client data for additional dimension members that are related to the particular dimension member. When client data is compared to external benchmark data, a client result value may be computed based on a multidimensional operation and a benchmark result value may be determined based on a mapping of client dimension members to benchmark dimension members. The result values may be provided by a server to a client, which may generate a graphical user interface (GUI) based on the result values.

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: Systems and methods of dynamically identifying supported items in an application are described. In one example, an analytics engine receives an indication of available client data, a user's enterprise role, and/or a user's security level. The analytics engine identifies a first subset of supported semantic items (e.g., business topics, business topics, measures, etc.) and a second subset of unsupported semantic items. For example, a semantic item may be supported if corresponding client data is available for analysis and the user's role/security level enable access to the client data. The analytics engine may send data including the supported semantic items and excluding the unsupported semantic items to an application.

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: Systems and methods of performing comparisons of client data and benchmark data are described. When client data is compared to internal benchmark data, comparative result values may be computed by performing a multidimensional operation with respect to client data for a particular dimension member and client data for additional dimension members that are related to the particular dimension member. When client data is compared to external benchmark data, a client result value may be computed based on a multidimensional operation and a benchmark result value may be determined based on a mapping of client dimension members to benchmark dimension members. The result values may be provided by a server to a client, which may generate a graphical user interface (GUI) based on the result values.

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: Systems and methods of dynamically identifying supported items in an application are described. In one example, an analytics engine receives an indication of available client data, a user's enterprise role, and/or a user's security level. The analytics engine identifies a first subset of supported semantic items (e.g., business topics, business topics, measures, etc.) and a second subset of unsupported semantic items. For example, a semantic item may be supported if corresponding client data is available for analysis and the user's role/security level enable access to the client data. The analytics engine may send data including the supported semantic items and excluding the unsupported semantic items to an application.

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.

Abstract: A particular method includes selecting a subset of a plurality of dimension members of a multi-dimensional data set. The method also includes computing a plurality of dimensional scores for the dimension members in the selected subset. Each dimensional score is associated with a particular dimension member in the subset and is a measure of a dimensional influence of the associated dimension member on a metric associated with the multi-dimensional data set. A dimension member with greater dimensional influence affects a value of the metric over a population more than a dimension member with less dimensional influence. The method further includes ranking the dimension members in the selected subset based on the dimensional scores.

Abstract: A particular method includes selecting a subset of a plurality of dimension members of a multi-dimensional data set. The method also includes computing a plurality of dimensional scores for the dimension members in the selected subset. Each dimensional score is associated with a particular dimension member in the subset and is a measure of a dimensional influence of the associated dimension member on a metric associated with the multi-dimensional data set. A dimension member with greater dimensional influence affects a value of the metric over a population more than a dimension member with less dimensional influence. The method further includes ranking the dimension members in the selected subset based on the dimensional scores.

Abstract: Embodiments of apparatus, systems and methods facilitate performing on-demand cluster analysis on a multi-dimensional data set comprising a plurality of dimension members. In some embodiments, the method comprises: selecting a subset of the plurality of dimension members based on a current context associated with the multi dimensional data set; computing a plurality of dimensional scores for a plurality of dimension members in the selected subset, wherein each dimensional score is associated with a distinct dimension member and measures the dimensional influence of the associated dimension member on a metric associated with the multi-dimensional data set; and ranking the plurality of dimension members in the selected subset based on the dimensional score.

Abstract: Embodiments of apparatus, systems and methods facilitate performing on-demand cluster analysis on a multi-dimensional data set comprising a plurality of dimension members. In some embodiments, the method comprises: selecting a subset of the plurality of dimension members based on a current context associated with the multi dimensional data set; computing a plurality of dimensional scores for a plurality of dimension members in the selected subset, wherein each dimensional score is associated with a distinct dimension member and measures the dimensional influence of the associated dimension member on a metric associated with the multi-dimensional data set; and ranking the plurality of dimension members in the selected subset based on the dimensional score.