Business Objects Software Limited

Business Objects S.a.

John MacGregor

Arindam Bhattacharjee

Anil Babu Ankisettipalli

Ashok Kumar Kn

Shipra Chaudhary

In one embodiment, data records associated with attributes are received. A check is made to determine whether the data records are greater than a maximum data record limit of a graph. Further, when the maximum number of data records in the bin is less than or equal to the maximum data record limit of the graph, the data records are retrieved and presented in the graph. When the data records are greater than the maximum data record limit of the graph, the data records are grouped into bins based on initial bin sizes corresponding to the plurality of attributes. Furthermore, weighted densities of the bins are determined using a maximum number of data records associated with a bin and a maximum data record limit of the bin. Further, the graph is rendered to present the weighted densities of the bins using a randomization technique to analyze the data records.

Presenting data records based on binning and randomization

A computer readable storage medium includes executable instructions to analyze a categorical dimension of multi-dimensional data as a function of entropy to form entropy results. The entropy results are plotted as a stacked bar chart. A user graphically navigates through the stacked bar chart.

Guided graphical navigation through multidimensional databases

A computer-readable medium to direct a computer to function in a specified manner includes executable instructions to: generate a set of qualitative assessments; convert the set of qualitative assessments into a set of quantitative assessments; produce a quantitative forecast from the set of quantitative assessments; and translate the quantitative forecast to a qualitative forecast.

Apparatus and method for forecasting qualitative assessments

Disclosed is a computer method and system for identifying significance of patterns across a plurality of data patterns, which involves identifying pattern types of the plurality of data patterns, determining a relative pattern significance factor to compare the pattern types. Determining the relative pattern significance factor further involves calculating a percentage change of an identified outlier from a median for a outlier pattern, calculating a value of a step change as a percentage of a last value of a step preceding the step change for a step change pattern and calculating a percentage change from a start value on the fitted curve to an end value on the fitted curve for a trend pattern. A ranked list of the pattern types are returned based on their corresponding relative pattern significant factors.

Identifying patterns of significance in numeric arrays of data

A computer-readable storage medium to direct a computer to function in a specified manner includes executable instructions to: process a balanced scorecard to generate a hierarchical tree; classify the parent to child relationships in the hierarchical tree; identify the quantitative associations among the parent to child relationships; and assess the balance within the balanced scorecard.

Apparatus and method for quantitatively measuring the balance within a balanced scorecard

A computer readable storage medium comprises executable instructions to define and calculate a plurality of performance indicators, including key performance indicators. A validity index for a key performance indicator is determined based on variances of performance indicators that are in conflict with the key performance indicator.

Apparatus and method for determining a validity index for key performance indicators

A computer-readable medium to direct a computer to function in a specified manner includes executable instructions to construct a strategy map characterizing a presumed causal link between a first business metric and a second business metric. A first pattern within data associated with the first business metric is identified; a second pattern within data associated with the second business metric is located. Any relationship between the first pattern and the second pattern is specified.

Apparatus and method for strategy map validation and visualization

An apparatus to identify patterns in a multi-dimensional database is described. In particular, a computer-readable medium to direct a computer to function in a specified manner is provided. The computer-readable medium comprises instructions to automatically identify a plurality of patterns associated with data comprising the multi-dimensional database and instructions to indicate the plurality of patterns that are identified. Exemplary patterns that can be identified include an outlier pattern, a step pattern, a random pattern, a trend pattern, and a periodic pattern.

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Ascot, United Kingdom

John MacGregor