Identifying patterns of significance in numeric arrays of data

• US 8,166,064 B2
• Filed: 05/06/2009
• Issued: 04/24/2012
• Est. Priority Date: 05/06/2009
• Status: Active Grant

• Current Assignee: Business Objects Incorporated (SAP SE)
• Original Assignee: Business Objects Incorporated (SAP SE)
• Inventors: MacGregor, John
• Primary Examiner(s): Kim, Charles
• Assistant Examiner(s): McKay, Douglas
• Application Number:
  US12/436,152
• Publication Number:
  US 20100287153A1
• Time in Patent Office: 1,084 Days
• Field of Search: 707/707, 707/738
• US Class Current: 707/776
• CPC Class Codes: G06F 16/283 Multi-dimensional databases or data warehouses, e.g. MOLAP or ROLAP
• CPC Class Codes: G06F 18/2433 Single-class perspective, e.g. one-against-all classification; Novelty detection; Outlier detection
• CPC Class Codes: G06F 2218/12 Classification; Matching

Abstract

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.

Claims

1. An article of manufacture, comprising:

a non-transitory machine readable storage medium comprising instructions which when executed by a machine cause the machine to perform operations comprising;

based on a query performed on a multidimensional data source, receiving a data set including a plurality of data patterns;

identifying, by a processor, a pattern type of each data set, to determine corresponding pattern significance factors associated with the plurality of data patterns, the determining comprising;

when the pattern type is an outlier pattern, calculating a percentage change of an identified outlier from a median;

when the pattern type is a step change pattern, calculating a percentage of a step change between a last value of a step and a first value preceding the step, wherein the step corresponds to an outlier in a first difference on the data set; and

when the pattern type is a trend pattern, calculating a degree of freedom factor, accepting a fitted curve approximation based on the degree of freedom factor and calculating a percentage change from a start value on the fitted curve to an end value on the fitted curve; and

comparing the calculated pattern significance factors of the outlier pattern, the step change pattern and the trend pattern to display a ranked list of the pattern types based on their corresponding pattern significance factors on a user interface.

2. The article of manufacture in claim 1, wherein determining the trend pattern comprises determining a linear regression for the data set for a linear fitted curve.

3. The article of manufacture in claim 1, wherein determining the trend pattern comprises determining a non-linear regression for the data set for a non-linear fitted curve.

4. The article of manufacture in claim 3, wherein determining the non-linear regression for the data set comprises transforming the non-linear regression to a linear regression.

5. The article of manufacture in claim 3, wherein the non-linear regression pattern is selected from a geometric regression pattern, a natural logarithm regression pattern or an exponential regression pattern.

6. The article of manufacture in claim 1, wherein calculating the percentage of the step change further comprises a difference between a first average of values after a step and a second average for values after the step.

7. The article of manufacture in claim 1, wherein the data set is included in the multi-dimensional data source.

8. The article of manufacture in claim 1, wherein calculating the percentage change from the start value on the fitted curve to the end value on the fitted curve comprises accepting the fitted curve if the value of freedom factor is more than 95% of the freedom factor.

9. A computer implemented method to identify significance of patterns across a plurality of data patterns, the method comprising:

based on a query performed on a multidimensional data source, receiving a data set including a plurality of data patterns;

identifying, by a processor, a pattern type of each data set, to determine corresponding pattern significance factors associated with the plurality of data patterns, the determining comprising;

when the pattern type is an outlier pattern, calculating a percentage change of an identified outlier from a median;

when the pattern type is a step change pattern, calculating a percentage of a step change between a last value of a step and a first value preceding the step, wherein the step corresponds to an outlier in a first difference on the data set; and

when the pattern type is a trend pattern, calculating a degree of freedom factor, accepting a fitted curve approximation based on the degree of freedom factor and calculating a percentage change from a start value on the fitted curve to an end value on the fitted curve; and

comparing the calculated pattern significance factors of the outlier pattern, the step chance pattern and the trend pattern to display a ranked list of the pattern types based on their corresponding pattern significance factors on a user interface.

10. The computer implemented method of claim 9, wherein determining the trend pattern comprises a linear regression for the data set.

11. The computer implemented method of claim 9, wherein determining the trend pattern comprises a non-linear regression for the data set.

12. The computer implemented method of claim 11, wherein determining the non-linear regression for the data set comprises transforming the non-linear regression to a linear regression.

13. The computer implemented method of claim 11, wherein the non-linear regression comprises a geometric regression.

14. The computer implemented method of claim 11, wherein the non-linear regression comprises a natural logarithm regression.

15. The computer implemented method of claim 11, wherein the non-linear regression comprises an exponential regression.

16. The computer implemented method of claim 9, wherein displaying a ranked list based on their corresponding pattern significance factors comprises displaying the ranked list in a table format.

17. A computer system to identify significance of patterns across a plurality of data patterns, comprising:

a processor to read and execute instructions stored in one or more memory elements; and

the one or more memory elements storing instructions related to;

a multidimensional data source to render a data set including a plurality of data patterns, based on a query performed;

a pattern type identifier module to identify a pattern type of each data set to determine corresponding pattern significance factors associated with the plurality of data patterns;

a pattern significance calculator in communication with the pattern type identifier to

determine corresponding pattern significance factors by

calculating a percentage change of an identified outlier from a median when the pattern type is an outlier pattern;

calculating a percentage of a step change between a last value of a step and a first value preceding the step, wherein the step corresponds to an outlier in a first difference on the data set, when the pattern type is a step change pattern;

calculating a degree of freedom factor, accepting a fitted curve approximation based on the degree of freedom factor and calculating a percentage change from a start value on the fitted curve to an end value on the fitted curve, when the pattern type is a trend pattern; and

compare the plurality of pattern significance factors of the outlier pattern, the step change pattern and the trend pattern; and

a user interface in communication with the pattern significance calculator to display a ranked list of the pattern types based on the corresponding pattern significance factors.