公开(公告)号：US20190212886A1

公开(公告)日：2019-07-11

申请号：US16359735

申请日：2019-03-20

Applicant: Business Objects Software Ltd.

Inventor： Flavia Moser ,  Scott Cameron ,  Julian Gosper

IPC: G06F3/0482 ,  G06F16/248

CPC classification number: G06F3/0482 ,  G06F16/248

Abstract: In some example embodiments, an indication of a selected data point of a current visualization can be received. A context of the selected data point can be determined based on a dimension of the data point, and explanation candidates can be generated based on the context of the selected data point. Each exploration candidate can have a different dimension context that is within the context of the selected data point and a corresponding value for the dimension context. For each one of the explanation candidates, a corresponding score can be generated based on a difference between the value for the explanation candidate and an average value of all the values of the explanation candidates. The explanation candidates can be ranked based on the scores. At least one of the explanation candidates can be selected based on the ranking, and selectable explanation(s) for the selected explanation candidate(s) can be displayed.

公开(公告)号：US20220004291A1

公开(公告)日：2022-01-06

申请号：US17476055

申请日：2021-09-15

Applicant: Business Objects Software Ltd.

Inventor： Flavia Moser ,  Scott Cameron ,  Julian Gosper

IPC: G06F3/0482 ,  G06F16/248

Abstract: In some example embodiments, an indication of a selected data point of a current visualization can be received. A context of the selected data point can be determined based on a dimension of the data point, and explanation candidates can be generated based on the context of the selected data point. Each exploration candidate can have a different dimension context that is within the context of the selected data point and a corresponding value for the dimension context. For each one of the explanation candidates, a corresponding score can be generated based on a difference between the value for the explanation candidate and an average value of all the values of the explanation candidates. The explanation candidates can be ranked based on the scores. At least one of the explanation candidates can be selected based on the ranking, and selectable explanation(s) for the selected explanation candidate(s) can be displayed.

公开(公告)号：US11567634B2

公开(公告)日：2023-01-31

申请号：US17476055

申请日：2021-09-15

Applicant: Business Objects Software Ltd.

Inventor： Flavia Moser ,  Scott Cameron ,  Julian Gosper

IPC: G06F3/0482 ,  G06F16/248

Abstract: In some example embodiments, an indication of a selected data point of a current visualization can be received. A context of the selected data point can be determined based on a dimension of the data point, and explanation candidates can be generated based on the context of the selected data point. Each exploration candidate can have a different dimension context that is within the context of the selected data point and a corresponding value for the dimension context. For each one of the explanation candidates, a corresponding score can be generated based on a difference between the value for the explanation candidate and an average value of all the values of the explanation candidates. The explanation candidates can be ranked based on the scores. At least one of the explanation candidates can be selected based on the ranking, and selectable explanation(s) for the selected explanation candidate(s) can be displayed.

公开(公告)号：US11137880B2

公开(公告)日：2021-10-05

申请号：US16359735

申请日：2019-03-20

Applicant: Business Objects Software Ltd.

Inventor： Flavia Moser ,  Scott Cameron ,  Julian Gosper

IPC: G06F3/0482 ,  G06F16/248

Abstract: In some example embodiments, an indication of a selected data point of a current visualization can be received. A context of the selected data point can be determined based on a dimension of the data point, and explanation candidates can be generated based on the context of the selected data point. Each exploration candidate can have a different dimension context that is within the context of the selected data point and a corresponding value for the dimension context. For each one of the explanation candidates, a corresponding score can be generated based on a difference between the value for the explanation candidate and an average value of all the values of the explanation candidates. The explanation candidates can be ranked based on the scores. At least one of the explanation candidates can be selected based on the ranking, and selectable explanation(s) for the selected explanation candidate(s) can be displayed.

公开(公告)号：US10896204B2

公开(公告)日：2021-01-19

申请号：US16266892

申请日：2019-02-04

Applicant: Business Objects Software Ltd.

Inventor： Flavia Moser ,  Alexander Kennedy MacAulay ,  Julian Gosper

IPC: G06F16/28 ,  G06F16/25 ,  G06F16/26 ,  G06F16/248 ,  G06T11/20

Abstract: A raw dataset including measures and dimensions is processed, by a preprocessing module, using an algorithm that produces a preprocessed dataset such that at least one type of statistical analysis of the preprocessed dataset yields equal results to the same type of statistical analysis of the raw dataset. The preprocessed dataset is then analyzed by a statistical analysis module to identify subsets of the preprocessed dataset that include a non-random structure or pattern. The analysis of the preprocessed dataset includes the at least one type of statistical analysis that produces the same results for both the preprocessed and raw datasets. The identified subsets are then ranked by a statistical ranker based on the analysis of the preprocessed dataset and a subset is selected for visualization based on the rankings. A visualization module then generates a visualization of the selected identified subset that highlights a non-random structure of the selected subset.

公开(公告)号：US20190243844A1

公开(公告)日：2019-08-08

申请号：US16266892

申请日：2019-02-04

Applicant: Business Objects Software Ltd.

Inventor： Flavia Moser ,  Alexander Kennedy MacAulay ,  Julian Gosper

IPC: G06F16/28 ,  G06F16/25 ,  G06T11/20 ,  G06F16/26 ,  G06F16/248

CPC classification number: G06F16/283 ,  G06F16/248 ,  G06F16/254 ,  G06F16/26 ,  G06T11/206

Abstract: A raw dataset including measures and dimensions is processed, by a preprocessing module, using an algorithm that produces a preprocessed dataset such that at least one type of statistical analysis of the preprocessed dataset yields equal results to the same type of statistical analysis of the raw dataset. The preprocessed dataset is then analyzed by a statistical analysis module to identify subsets of the preprocessed dataset that include a non-random structure or pattern. The analysis of the preprocessed dataset includes the at least one type of statistical analysis that produces the same results for both the preprocessed and raw datasets. The identified subsets are then ranked by a statistical ranker based on the analysis of the preprocessed dataset and a subset is selected for visualization based on the rankings. A visualization module then generates a visualization of the selected identified subset that highlights a non-random structure of the selected subset.