Visualization of parallel co-ordinates

• US 9,153,051 B2
• Filed: 03/01/2013
• Issued: 10/06/2015
• Est. Priority Date: 03/01/2013
• Status: Active Grant

• Current Assignee: Business Objects Incorporated (SAP SE)
• Original Assignee: Business Objects Incorporated (SAP SE)
• Inventors: Ankisettipalli, Anil Babu, Kumar KN, Ashok
• Primary Examiner(s): Xiao, Ke
• Assistant Examiner(s): Imperial, Jed-Justin
• Application Number:
  US13/781,762
• Publication Number:
  US 20140247267A1
• Time in Patent Office: 949 Days
• Field of Search: None
• US Class Current: 1/1
• CPC Class Codes: G06T 11/206 Drawing of charts or graphs
• CPC Class Codes: G16B 45/00 ICT specially adapted for bioinformatics-related data visualisation, e.g. displaying of maps or networks

Abstract

Parallel co-ordinates associated with a business scenario represent underlying data of the business scenario. Based upon associated categorical variables, the parallel co-ordinates are divided into a plurality of segments. Each segment includes a pair of continuous variables represented as a pair of axes of the parallel co-ordinates. The pairs of axes of the parallel co-ordinates are fragmented to generate bi-variant bins. The bi-variant bines include continuous values corresponding to each fragment of the pair of axis. An associated backend database is queried to determine aggregated tuples of continuous values between each bi-variant bin of the corresponding pair of axes. Based on the aggregated tuples, a statistical visualization of the aggregated tuples between the bi-variant bins for the axes is generating. The statistical visualization includes a distribution of the tuples for the plurality of segments corresponding to the parallel co-ordinates.

Claims

1. A computer implemented method to generate a visualization of parallel coordinates, comprising:

based upon one or more categorical variables associated with parallel co-ordinates of a business scenario, a processor of a computer dividing the parallel co-ordinates into a plurality of segments, each segment including a pair of continuous variables represented as a pair of axes of the parallel co-ordinates;

fragmenting the pairs of axes to generate one or more bi-variant bins including one or more continuous values corresponding to each fragment of the pair of axes;

querying a backend database associated with the parallel co-ordinates to determine one or more aggregated tuples of continuous values between the bi-variant bin of the corresponding pair of axes, the aggregated tuples are determined by;

determining a count of tuples corresponding to each bi-variant bin by aggregating corresponding number of continuous values;

determining an aggregation of continuous values corresponding to each bi-variant bin by aggregating the corresponding continuous values; and

storing the count of continuous values and the aggregation of continuous values as

aggregated tuples; and

generating a statistical visualization of the aggregated tuples between the bi-variant bins for the axes associated with the parallel co-ordinates on a computer generated user interface, the statistical visualization including a distribution of the tuples for the plurality of segments corresponding to the parallel co-ordinates.

2. The computer implemented method of claim 1 further comprising:

receiving a selection of the parallel co-ordinates representing multivariate data associated with a business scenario, the parallel co-ordinates including one or more polylines, the one or more axes, and one or more vertices on the axes;

determining the categorical variables and the continuous variables associated with the multivariate data of the parallel co-ordinates, residing in the backend database;

assigning the categorical variables to one or more groups of polylines of the parallel co-ordinates; and

assigning the continuous variables to the axes of the parallel co-ordinates, and assigning the continuous values to the vertices of the axes of the parallel co-ordinates.

3. The computer implemented method of claim 1, wherein dividing the parallel coordinates include:

dividing the parallel co-ordinates into the segments at each axis, wherein each segment represents a data flow between a pair of axes associated with the corresponding segment.

4. The computer implemented method of claim 1, wherein fragmenting the pair of axes comprises:

fragmenting a first axis of the corresponding segment into a first set of finite bins, each bin representing the attributing continuous values associated with the continuous variable corresponding to the first axis;

fragmenting a second axis of the corresponding segment into a second set of finite bins, each bin representing the attributing continuous values associated with the continuous variable corresponding to the second axis; and

generating the bi-variant bins by consolidating the bins from the first axis and the bins from the second axis.

5. The computer implemented method of claim 4, wherein fragmenting further comprises:

determining a range of the continuous values associated with the corresponding axis, and

dividing the range of continuous values into a finite number of bins.

6. The computer implemented method of claim 1 further comprising:

a backend database table representing the categorical variables, the continuous variables and the continuous values associated with the parallel co-ordinates.

7. The computer implemented method of claim 1 further comprising:

generating one or more database buckets for the backend database table at an in-database aggregation level, wherein the database buckets correspond to the bi-variant bins.

8. The computer implemented method of claim 1 further comprising:

in-database aggregation mechanism to determine the aggregated tuples.

9. The computer implemented method of claim 1 further comprising:

categorizing the segments based upon the categorical variables, to select individual categories of the segments for fragmenting the corresponding pairs of axes; and

consolidating the individual categories to generate the statistical visualization.

10. The computer implemented method of claim 1, wherein generating the statistical visualization comprises:

generating a distribution of the tuples for each segment to represent a data flow between the pairs of axes corresponding to the segment;

generating the distribution of tuples for the plurality of segments to represent a data flow for all the segments; and

implementing bi-variant plotting of the tuples for the plurality of segments corresponding to the parallel co-ordinates.

11. The computer implemented method of claim 1 further comprising:

determining a density of the aggregated tuples for each bi-variant bin; and

plotting a degree of density of the aggregated tuples for each bi-variant bin in the statistical visualization.

12. The computer implemented method of claim 1, wherein the bi-variant bins represent two degrees of variation, comprising:

a first degree associated with a first axis of a corresponding segment; and

a second degree associated with a second axis of the corresponding segment.

13. An article of manufacture including a non-transitory computer readable storage medium to tangibly store instructions, which when executed by a computer, cause the computer to:

divide parallel co-ordinates into a plurality of segments based upon one or more categorical variables associated with the parallel co-ordinates, each segment including a pair of continuous variables represented as a pair of axes of the parallel co-ordinates;

fragment the pairs of axes to generate one or more bi-variant bins including one or more continuous values corresponding to each fragment of the axes;

query a backend database associated with the parallel co-ordinates to determine one or more aggregated tuples of continuous values between the bi-variant bins of the corresponding pair of axes, the aggregated tuples are determined by;

determining a count of tuples corresponding to each bi-variant bin by aggregating corresponding number of continuous values;

determining an aggregation of continuous values corresponding to each bi-variant bin by aggregating the corresponding continuous values; and

storing the count of continuous values and the aggregation of continuous values as aggregated tuples; and

generate a statistical visualization of the aggregated tuples between the bi-variant bins for the axes associated with the parallel co-ordinates, the statistical visualization including a distribution of the tuples for the plurality of segments corresponding to the parallel co-ordinates.

14. The article of manufacture of claim 13 further including instructions to:

receive a selection of the parallel co-ordinates representing multivariate data associated with a business scenario, the parallel co-ordinates including one or more polylines, the one or more axes, and one or more vertices on the axes;

determine the categorical variables and the continuous variables associated with the multivariate data of the parallel co-ordinates, residing in the backend database;

assign the categorical variables to one or more groups of polylines of the parallel co-ordinates; and

assign the continuous variables to the axes of the parallel co-ordinates, and assigning the continuous values to the vertices of the axes of the parallel co-ordinates.

15. The article of manufacture of claim 13, wherein fragmenting the pair of axes further include instructions to:

fragment a first axis of the corresponding segment into a finite number of bins, each bin representing the attributing continuous values associated with the continuous variable corresponding to the first axis;

fragment a second axis of the corresponding segment into a finite number of bins, each bin representing the attributing continuous values associated with the continuous variable corresponding to the second axis; and

generate the bi-variant bins by consolidating the bins from the first axis and the bins from the second axis.

16. The article of manufacture of claim 13, wherein generating the statistical visualization further include instructions to:

generate a distribution of the tuples for each segment to represent a data flow between the pairs of axes corresponding to the segment;

generate the distribution of tuples for the plurality of segments to represent a data flow for all the segments; and

implement bi-variant plotting of the tuples for the plurality of segments corresponding to the parallel co-ordinates.

17. A computer system to generate a generate a visualization of parallel co-ordinates, comprising:

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

the one or more memory elements storing instructions related to—

divide the parallel co-ordinates into a plurality of segments based upon one or more associated categorical variables, each segment including a pair of continuous variables represented as a pair of axes of the parallel co-ordinates;

fragment the pairs of axes to generate one or more bi-variant bins including one or more continuous values corresponding to each fragment of the axes;

query a backend database associated with the parallel co-ordinates to determine one or more aggregated tuples of continuous values between each bi-variant bin of the corresponding pair of axes, the aggregated tuples are determined by;

determining a count of tuples corresponding to each bi-variant bin by aggregating corresponding number of continuous values;

determining an aggregation of continuous values corresponding to each bi-variant bin by aggregating the corresponding continuous values; and

storing the count of continuous values and the aggregation of continuous values as aggregated tuples; and

generate a statistical visualization of the aggregated tuples between the bi-variant bins for the axes associated with the parallel co-ordinates on a computer generated user interface, the statistical visualization including a distribution of the tuples for the plurality of segments corresponding to the parallel co-ordinates.

18. The computer system of claim 17 further including:

a user interface component comprising —

a user interface engine to trigger the statistical visualization, a parameter selection module to select a categorical value for analysis, and a parameter assignment module to assign the categorical variables and the continuous variables to the parallel co-ordinates;

an in-database aggregation component comprising—

a query module to query the backend database and determine aggregated tuples of continuous values between the bi-variant bins of the corresponding pair of axes,

a separation module to divide the parallel co-ordinates into segment and fragment the segments to generate the bi-variant bins, and

an analysis module to generates a statistical visualization of the aggregated tuples; and

a backend database comprising —

an aggregation module to determine the aggregated tuples, a tuple storage module to store metadata associated with the tuples, and a bin storage module to store metadata associated with the fragmentation of the parallel co-ordinates.