Analysis and visualization tool utilizing mixture of multiple reliability measures for product and part combinations

• US 10,275,408 B1
• Filed: 03/27/2015
• Issued: 04/30/2019
• Est. Priority Date: 03/27/2015
• Status: Active Grant

• Current Assignee: Emc IP Holding Company LLC (Dell Technologies Inc.)
• Original Assignee: Emc IP Holding Company LLC (Dell Technologies Inc.)
• Inventors: Cohen, Raphael, Dionisio, David M., Le, Phu Son, Liang, Hengli, Beazley, Jenny L.
• Primary Examiner(s): Thomas, Ashish
• Assistant Examiner(s): Daud, Abdullah A
• Application Number:
  US14/670,976
• Time in Patent Office: 1,495 Days
• CPC Class Codes: G06F 16/248 Presentation of query results
• CPC Class Codes: G06F 16/254 Extract, transform and load [ETL] procedures, e.g. ETL data flows in data warehouses

Abstract

An apparatus comprises a processing platform configured to implement an analysis and visualization tool utilizing a mixture of multiple reliability measures to characterize each of a plurality of product and part combinations. The analysis and visualization tool comprises a data aggregation module configured to extract product and part data from a big data repository, a reliability measure generator configured to process the extracted product and part data to generate a plurality of reliability measures for each of a plurality of different product and part combinations, a mixture model module configured to compute a score from the plurality of reliability measures for each of the different product and part combinations, and a visualization module configured to generate at least one visualization as a function of the scores computed for the respective different product and part combinations. The generated visualization may comprise a quadrant plot view visualization displaying the computed scores.

Claims

1. An apparatus comprising:

a processing platform comprising one or more processing devices each comprising a processor coupled to a memory;

wherein the processing platform is configured;

to extract product and part data from a big data repository;

to process the extracted product and part data to generate a plurality of reliability measures for each of a plurality of different product and part combinations;

to compute a score from the plurality of reliability measures for each of the different product and part combinations;

to generate at least one visualization as a function of the scores computed for the respective different product and part combinations; and

responsive to detecting one or more data conditions associated with an aberration in the generated visualization, to provide an alert indicating the aberration to a user;

wherein the plurality of reliability measures for each of the plurality of different product and part combinations comprises;

at least one retrospective reliability measure characterizing at least one of a failure rate and a mean age at failure; and

at least one trend reliability measure characterizing a failure rate trend;

wherein the generated visualization comprises two or more distinct display elements each associated with a corresponding set of one or more product and part combinations for a designated time period;

wherein a given one of the display elements comprises a first display characteristic indicating the one or more scores computed for its associated set of one or more product and part combinations and a second display characteristic indicating a number of instances of the product and part combinations in its associated set of one or more product and part combinations that are in field during the designated time period;

wherein the first display characteristic comprises at least one of a shading and a color of the given display element;

wherein the second display characteristic comprises a size of the given display element relative to one or more other display elements;

wherein the processing platform is further configured to adjust the generated visualization such that the first display characteristic of the given display element represents one of the at least one retrospective reliability measure and the at least one trend reliability measure for its associated set of one or more product and part combinations; and

wherein adjusting the generated visualization comprises dynamically adjusting the generated visualization in response to user input to change the first display characteristic from (i) representing the one or more scores computed as a combination of the at least one retrospective reliability measure and the at least one trend measure to (ii) representing scores computed using one of the at least one retrospective reliability measure and the at least one trend measure.

2. The apparatus of claim 1 wherein the processing platform is configured to weight respective ones of the plurality of reliability measures in computing the score for a given one of the product and part combinations.

3. The apparatus of claim 1 wherein the product and part data extracted from the big data repository is aggregated using predetermined data elements including at least a subset of product name, part name, product instance identifier, time period, customer, number of parts in field, number of parts added, number of parts failed, total days to failure and total days in field.

4. The apparatus of claim 1 wherein the plurality of reliability measures comprise two or more of failure rate, mean time to failure, change in failure rate and annual replacement rate.

5. The apparatus of claim 1 wherein the generated visualization comprises a quadrant plot view visualization.

6. The apparatus of claim 5 wherein the quadrant plot view visualization displays the scores computed for respective product and part combinations as a function of number of parts in field for a designated time period.

7. The apparatus of claim 5 wherein each of a plurality of points plotted in the quadrant plot view visualization corresponds to a different one of the product and part combinations.

8. The apparatus of claim 1 wherein the generated visualization is configured to support drill down functionality into at least one of a particular product and a particular part of the generated visualization.

9. The apparatus of claim 1 wherein the generated visualization is configured to permit selection of one or more particular products from among a plurality of product and part combinations for which scores are presented in the generated visualization and wherein responsive to selection of one or more particular products the generated visualization is modified to show only those of the scores that relate to the selected product or products.

10. The apparatus of claim 1 wherein the first display characteristic is used to indicate one of failure rate and failure rate trend.

11. An information processing system comprising the apparatus of claim 1.

18. The apparatus of claim 1 wherein adjusting the generated visualization comprises utilizing one or more designated shadings or colors to highlight a selected one of the at least one retrospective reliability measure and the at least one trend reliability measure.

19. The apparatus of claim 1 wherein adjusting the generated visualization comprises utilizing one or more designated shadings or colors to highlight respective ones of the display elements whose associated set of one or more product and part combinations have particular desired values of a selected one of the at least one retrospective reliability measure and the at least one trend reliability measure.

12. A method comprising:

extracting product and part data from a big data repository;

processing the extracted product and part data to generate a plurality of reliability measures for each of a plurality of different product and part combinations;

computing a score from the plurality of reliability measures for each of the different product and part combinations;

generating at least one visualization as a function of the scores computed for the respective different product and part combinations;

responsive to detecting one or more data conditions associated with an aberration in the generated visualization, providing an alert indicating the aberration to a user;

wherein the plurality of reliability measures for each of the plurality of different product and part combinations comprises;

at least one retrospective reliability measure characterizing at least one of a failure rate and a mean age at failure; and

at least one trend reliability measure characterizing a failure rate trend;

wherein the generated visualization comprises two or more distinct display elements each associated with a corresponding set of one or more product and part combinations for a designated time period;

wherein a given one of the display elements comprises a first display characteristic indicating the one or more scores computed for its associated set of one or more product and part combinations and a second display characteristic indicating a number of instances of the product and part combinations in its associated set of one or more product and part combinations that are in field during the designated time period;

wherein the first display characteristic comprises at least one of a shading and a color of the given display element;

wherein the second display characteristic comprises a size of the given display element relative to one or more other display elements;

adjusting the generated visualization such that the first display characteristic of the given display element represents one of the at least one retrospective reliability measure and the at least one trend reliability measure for its associated set of one or more product and part combinations;

wherein adjusting the generated visualization comprises dynamically adjusting the generated visualization in response to user input to change the first display characteristic from (i) representing the one or more scores computed as a combination of the at least one retrospective reliability measure and the at least one trend measure to (ii) representing scores computed using one of the at least one retrospective reliability measure and the at least one trend measure; and

wherein the method is performed by a processing platform comprising one or more processing devices.

13. The method of claim 12 wherein generating at least one visualization comprises generating a quadrant plot view visualization and wherein the quadrant plot view visualization displays the scores computed for respective product and part combinations as a function of number of parts in field for the designated time period.

14. The method of claim 12 further comprising:

permitting selection of one or more particular products from among a plurality of product and part combinations for which scores are presented in the generated visualization; and

responsive to selection of one or more particular products, modifying the visualization to show only those of the scores that relate to the selected product or products.

15. A non-transitory processor-readable storage medium having program code of one or more software programs embodied therein, wherein the program code when executed by at least one processing device of a processing platform causes the processing device:

to extract product and part data from a big data repository;

to process the extracted product and part data to generate a plurality of reliability measures for each of a plurality of different product and part combinations;

to compute a score from the plurality of reliability measures for each of the different product and part combinations;

to generate at least one visualization as a function of the scores computed for the respective different product and part combinations; and

responsive to detecting one or more data conditions associated with an aberration in the generated visualization, to provide an alert indicating the aberration to a user;

wherein the plurality of reliability measures for each of the plurality of different product and part combinations comprises;

at least one retrospective reliability measure characterizing at least one of a failure rate and a mean age at failure; and

at least one trend reliability measure characterizing a failure rate trend;

wherein the generated visualization comprises two or more distinct display elements each associated with a corresponding set of one or more product and part combinations for a designated time period; and

wherein a given one of the display elements comprises a first display characteristic indicating the one or more scores computed for its associated set of one or more product and part combinations and a second display characteristic indicating a number of instances of the product and part combinations in its associated set of one or more product and part combinations that are in field during the designated time period;

wherein the first display characteristic comprises at least one of a shading and a color of the given display element;

wherein the second display characteristic comprises a size of the given display element relative to one or more other display elements;

adjusting the generated visualization such that the first display characteristic of the given display element represents one of the at least one retrospective reliability measure and the at least one trend reliability measure for its associated set of one or more product and part combinations; and

wherein adjusting the generated visualization comprises dynamically adjusting the generated visualization in response to user input to change the first display characteristic from (i) representing the one or more scores computed as a combination of the at least one retrospective reliability measure and the at least one trend measure to (ii) representing scores computed using one of the at least one retrospective reliability measure and the at least one trend measure.

16. The processor-readable storage medium of claim 15 wherein the storage medium comprises at least one of an electronic memory and a storage disk.

17. The processor-readable storage medium of claim 15 wherein the program code when executed by at least one processing device further causes said processing device:

to generate said at least one visualization as a quadrant plot view visualization configured to display the scores computed for respective product and part combinations as a function of number of parts in field for the designated time period.