Business Objects Software Limited

Sap Se

Semuel Kadarusman

Justin Wong

Veljko Jovanovic

Patrick Wang

Ping Xiang

Alfred Fung

Rui Liu

Clarence Chuahuico

Techniques for validation against aggregation across different versions of data obtain a selection of a data set, a dimension configuration for a data visualization, and a filter configuration for the data visualization. Then it is determined whether the data visualization is valid based on a determination that the zero or more filter dimensions include the version dimension, or a determination that the zero or more selected dimensions include the version dimension, or a determination that the zero or more selected dimensions include at least one of the measurable dimensions and that all measurable dimensions of the selected dimensions are restricted to a single version of data from among the multiple versions of the same data. The data visualization is generated in response to a determination that the data visualization is valid.

Validation against aggregation across different versions of data

Some embodiments provide a program that generates a first query for a first visualization based on a first query model associated with the first visualization. The first query model includes a first reference to a shared object. The program further sends the first query to a computing system. The program also receives from the computing system a first set of data. The program further generates the first visualization based on the first set of data. The program also generates a second query for a second visualization based on a second query model associated with the second visualization. The second query model includes a second reference to the shared object. The program further sends the second query to the computing system. The program also receives from the computing system a second set of data. The program further generates the second visualization based on the second set of data.

Managing query models based on shared objects

A system includes reception of a filter on a first dimension of a first data model, determination of an association between a second dimension of the first data model and a first dimension of a second data model, generation of a calculated dimension based on the second dimension, the rows of the calculated dimension being identical to the rows of the first dimension, application of the filter to the rows of the calculated dimension to generate filtered rows of the calculated dimension, execution of an inner join between the filtered rows of the calculated dimension and a fact table of the second data model to generate a result table including one or more measure values of the second data model, and generation of a visualization including the result table.

Server-side cross-model filtering

Some embodiments provide execution of a query on a target data model which is filtered on a measure of a source data model, even if the source data model and the target data model are not logically linked. Some embodiments further support execution of a query on a target data model which is filtered on a measure of a source data model and on a dimension filter of another data model. Some embodiments provide for a substantial amount of query execution to occur on the backend, thereby freeing client resources in comparison to prior approaches.

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Burnaby, Canada

Semuel Kadarusman