METHOD AND SYSTEM FOR PERFORMANCE METRIC ANOMALY DETECTION IN TRANSPORTATION SYSTEMS

• US 20150199636A1
• Filed: 01/13/2014
• Published: 07/16/2015
• Est. Priority Date: 01/13/2014
• Status: Abandoned Application

• Current Assignee: Conduent Business Services, LLC (Conduent, Inc.)
• Original Assignee: Xerox Corporation (Xerox Holdings Corp.)
• Inventors: Gil, Alvaro E., Handley, John C.
• Application Number:
  US14/153,305
• Publication Number:
  US 20150199636A1
• Time in Patent Office: Days
• US Class Current: 1/1
• CPC Class Codes: G06Q 10/06393 Score-carding, benchmarking or key performance indicator [KPI] analysis
• CPC Class Codes: G06Q 50/40 Business processes related to the transportation industry shipping G06Q10/083

Abstract

A transportation service data assessment system includes a data set holding f performance metrics for various route components of a transportation service. When the system receives a selected set of operational data parameter labels, as well one or more route components, it develops a matrix of performance metrics corresponding to the operational data and route components, determines a distance between each row of the performance metric matrix to yield a multi-dimensional matrix, and maps the distance data to a 2-D or 3-D coordinate set so that to yield a coordinate matrix. The system groups the data of the third matrix into clusters and presents the clusters on a display so that outliers are visually distinguished from clustered items, and so that redundant items are also visually apparent in the clusters.

Claims

1. A method of assessing a transportation service monitoring system, comprising, by a processor:

accessing a data set comprising a plurality of performance metrics for each of a plurality of route components of a transportation service;

receiving a selected set of operational data parameter labels;

retrieving from the data set, for each of a selected set of route components, the performance metrics that correspond to the labels;

constructing a two-dimensional N×

M first matrix P of the performance metrics so that one dimension of the first matrix is a performance metric and each other dimension of the first matrix is a parameter value for a route component with which the performance metric is associated;

determining a distance between each row of the first matrix P and using the distance to construct a second matrix D;

mapping the second matrix D to a coordinate set to yield a third matrix C;

grouping data elements of third matrix C into clusters; and

presenting the clusters on a display so that data elements that are outliers are visually distinguished from data elements that are clustered.

2. The method of claim 1, wherein presenting the clusters on the display also visually identifies clustered data elements that represent redundant information.

3. The method of claim 1, further comprising, by the processor:

before mapping the second matrix D to the third matrix C, detecting which of the outliers are extreme outliers; and

when presenting the clusters on the display, also displaying the extreme outliers so that they are visually distinguished from the clusters and the other outliers.

4. The method of claim 3, wherein detecting the extreme outliers comprises applying robust principal component analysis to the second matrix D or to a function of the second matrix D.

5. The method of claim 4, wherein applying the robust principal component analysis is done to the function of the second matrix D, and the method further comprises generating the function of the second matrix D by:

constructing a matrix J=I<sub>N×

N</sub>−

(1, N)*1^T*1 where I is the identity matrix, N is the number of rows of first matrix P, and each 1 is a vector containing one row and N columns of ones;

determining D² as a square of every element in second matrix D;

constructing a matrix B=−

0.5*J*D²*J; and

using matrix B as the function of D to which the robust principal component analysis is applied.

6. The method of claim 1, further comprising, by the processor before constructing the second matrix D, constructing a transpose M×

N matrix P^T of matrix P and using the transpose matrix P^T in place of first matrix P when determining the distance between each row of the first matrix.

7. The method of claim 1, further comprising, by the processor:

from the data elements of third matrix C, identifying redundant data elements and non-redundant data elements;

constructing a transpose M×

N2 matrix P^T, where N2 includes none of the redundant data elements;

determining a distance between each row of the transpose matrix P^T;

using the determined distance between each row of the transpose matrix P^T to construct an updated second matrix D₂;

mapping the updated second matrix D₂ to a coordinate set to yield an updated third matrix C₂;

grouping data elements of matrix C₂ into clusters; and

presenting the clusters of matrix C₂ on the display so that data elements that are outliers are visually distinguished from data elements that are clustered.

8. The method of claim 1, wherein presenting the clusters on a display so that outlier data elements are visually distinguished from clustered data elements comprises spatially separating the outlier data elements from the clustered data elements.

9. The method of claim 1, wherein presenting the clusters on a display so that outliers are visually distinguished from clustered data elements comprises, for any data element that is clustered with the group in a displayed two dimensions but an outlier with respect to a third dimension, highlighting the data element in a color that is different from that of the other clustered data elements.

10. A method of assessing transportation service performance, comprising, by a processor:

accessing a data set comprising a plurality of performance metrics for each of a plurality of route components of a transportation service;

receiving a selected set of operational data parameter labels;

retrieving from the data set, for each of a selected set of route components, the performance metrics that correspond to the labels;

constructing a two-dimensional performance metric matrix using the retrieved performance metrics and parameter values;

using values of the two-dimensional performance metric matrix to construct a higher-dimensional matrix;

detecting extreme outliers from data in the higher-dimensional matrix using robust principal component analysis;

mapping the higher-dimensional matrix data to a coordinate matrix;

grouping data elements of the coordinate matrix into clusters; and

presenting the clusters on a display so that data elements that are non-extreme outliers are visually distinguished from data elements that are clustered, and extreme outliers are visually distinguished from both the clustered data elements and the non-extreme outliers.

11. The method of claim 10, wherein presenting the clusters on the display also visually identifies clustered data elements that represent redundant information.

12. The method of claim 10, wherein detecting the extreme outliers comprises applying robust principal component analysis to the second matrix D or to a function of the second matrix D.

13. The method of claim 10, wherein presenting the clusters on a display so that the extreme outliers data elements are visually distinguished from clustered data elements comprises spatially separating the outlier data elements from the clustered data elements.

14. The method of claim 10, wherein presenting the clusters on a display so that outliers are visually distinguished from clustered data elements comprises, for any data element that is clustered with the group in a displayed two dimensions but an outlier with respect to a third dimension, highlighting the data element in a color that is different from that of the other clustered data elements.

15. A transportation service performance assessment system, comprising:

a data set comprising a plurality of performance metrics for each of a plurality of route components of a transportation service;

a display;

a processor; and

a computer-readable medium containing programming instructions that, when executed, instruct the processor to;

receive a selected set of operational data parameter labels,

retrieve from the data set, for each of a selected set of route components, the performance metrics that correspond to the labels,

construct a two-dimensional performance metric matrix using the retrieved performance metrics and parameter values,

use values of the two-dimensional performance metric matrix to construct a higher-dimensional matrix,

detect extreme outliers from data in the higher-dimensional matrix using robust principal component analysis,

map the higher-dimensional matrix data to a coordinate matrix,

group data elements of the coordinate matrix into clusters, and

cause the clusters to be output on the display so that data elements that are non-extreme outliers are visually distinguished from data elements that are clustered, and so that data elements that are extreme outliers are visually distinguished from both the non-extreme outliers and the clustered data elements.

16. The system of claim 15, wherein the instructions that cause the clusters to be output on the display also comprise instructions to visually distinguish clustered data elements that represent redundant information.

17. The system of claim 15, further comprising additional instructions that, when executed, instruct the processor to, before mapping the higher-dimensional matrix to the coordinate matrix, apply robust principal component analysis to the higher-dimensional matrix or to a function of the higher-dimensional matrix.

18. The system of claim 17, wherein the instructions to apply the robust principal component analysis comprise instructions to do so to the function of the higher-dimensional matrix, and the instructions further comprises instructions to generate the function of the higher-dimensional matrix by:

constructing a matrix J=I<sub>N×

N</sub>−

(1, N)*1^T*1 where I is the identity matrix, N is the number of rows of first matrix P, and each 1 is a vector containing one row and N columns of ones;

determining D² as a square of every element in higher-dimensional matrix;

constructing a matrix B=−

0.5*J*D²*J; and

using matrix B as the function of D to which the robust principal component analysis is applied.

19. The system of claim 15, further comprising additional instructions that, when executed, instruct the processor to, before constructing the higher-dimensional matrix, construct a transpose M×

N matrix P^T of the two-dimensional performance metric matrix and using the transpose matrix P^T to determine the distance between each row of the two-dimensional performance metric matrix when constructing the higher-dimensional matrix.

20. The system of claim 15, further comprising additional instructions that, when executed, instruct the processor to, before causing the clusters to be output on the display:

from the data elements of coordinate matrix, identify redundant data elements and non-redundant data elements;

construct a transpose matrix P^T, that includes the non-redundant data elements and none of the redundant data elements;

determine a distance between each row of the transpose matrix P^T;

use the determined distance between each row of the transpose matrix P^T to construct an updated higher-dimensional metric;

map the updated higher-dimensional matrix to a coordinate set to yield an updated coordinate matrix;

group data elements of the updated coordinate matrix into clusters; and

when presenting the clusters of on the display, use the clusters from the updated coordinate matrix.

21. The system of claim 15, wherein the instructions to present the clusters on the display so that non-extreme outlier data elements are visually distinguished from clustered data elements comprise instructions to spatially separate the non-extreme outlier data elements from the clustered data elements.

22. The system of claim 15, wherein the instructions to present the clusters on a display so that non-extreme outliers are visually distinguished from clustered data elements comprise instructions to, for any data element that is clustered with the group in a displayed two dimensions but a non-extreme outlier with respect to a third dimension, highlight the data element in a color that is different from that of the other clustered data elements.