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: 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: 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: 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 computer readable medium includes executable instructions to integrate a write-back module in a report document, to publish to a recipient the report document and metadata identifying the recipient, and to update a data source based on received write-back information, including the metadata identifying the recipient.

Abstract: A computer readable medium includes executable instructions to integrate a write-back module in a report document, to publish to a recipient the report document and metadata identifying the recipient, and to update a data source based on received write-back information, including the metadata identifying the recipient.