Visier Solutions, Inc.

Geoffrey Benjamin Zenger

Ryan James Moore

Wenbo Jiang

Philipp Ziegler

Ryan Wayne Hanna

Chuen Yan Sit

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.

Systems and methods for generating event stream data

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.

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.

Dynamic identification of supported items in an application

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.

Systems and methods of mapping multidimensional data and executing queries

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.

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.

Apparatus, systems and methods for dynamic on-demand context sensitive cluster analysis

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.

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Coquitlam, Canada

Geoffrey Benjamin Zenger