METHOD AND SYSTEM FOR RATING MEASURED VALUES TAKEN FROM A SYSTEM

• US 20160239753A1
• Filed: 08/13/2014
• Published: 08/18/2016
• Est. Priority Date: 09/27/2013
• Status: Abandoned Application

• Current Assignee: Deutsche Telekom AG
• Original Assignee: Deutsche Telekom AG
• Inventors: Schuessler, Martin, Shahin, Tajik, Loehlein, Bernhard, Roshandel, Mehran, Ketabdar, Hamed
• Application Number:
  US15/024,365
• Publication Number:
  US 20160239753A1
• Time in Patent Office: Days
• US Class Current: 1/1
• CPC Class Codes: G06F 11/0721 within a central processing unit [CPU]
• CPC Class Codes: G06F 11/079 Root cause analysis, i.e. error or fault diagnosis in a hardware test environment G06F11/22; in a software test environment G06F11/36
• CPC Class Codes: G06F 17/18 for evaluating statistical data , e.g. average values, frequency distributions, probability functions, regression analysis forecasting specially adapted for a specific administrative, business or logistic context G06Q10/04
• CPC Class Codes: G06F 18/245 relating to the decision surface
• CPC Class Codes: G06N 20/00 Machine learning
• CPC Class Codes: G06N 7/01 Probabilistic graphical models, e.g. probabilistic networks

Abstract

A method for rating measured values taken from a system S that may be in an error-free or erroneous state, includes: forming, by a device, a set V of unmarked measured values v from the system S; forming, by the device, a modified learning set V′ comprising measured values v′ for a learning system L by removal or weighting or removal and weighting of measured values from the set V using a random-based method; forming, by the device, a model M for rating measured values from the system S by the learning system L from the modified learning set V′; and rating, by the device, measured values from the system S by a rating system B using the model M. At least one closest neighbor of the measured value v is removed during removal or weighting or removal and weighting of measured values v from the set V.

Claims

1. :

A method for rating measured values taken from a system S that may be in an error-free or erroneous state, wherein the system S comprises at least one communication network, a network component of a communication system or a service of a communication network, the method comprising;

(a) forming, by a device, a set V of unmarked measured values v from the system S;

(b) forming, by the device, a modified learning set V′

comprising measured values v′

for a learning system L by (i) removal or (ii) weighting or (iii) removal and weighting of measured values from the set V using a random-based method;

(c) forming, by the device, a model M for rating measured values from the system S by the learning system L from the modified learning set V′

; and

(d) rating, by the device, measured values from the system S by a rating system B using the model M;

wherein step (b) further comprises removing at least one closest neighbor of the measured value v during (i) removal or (ii) weighting or (iii) removal and weighting of measured values v from the set V.

2. :

The method according to claim 1, wherein step (b) further comprises;

(b1) forming a score value set Q comprising score values q from the set V by at least one score function F;

V→

Q, vF(v)=q;

(b2) forming a probability set P comprising probabilities p from the score value set Q by at least one transformation function T;

Q→

P, qT(q)=T(F(v))=p; and

(b3) forming the modified learning set V′

from measured values, wherein the measured values v∈

V are included with a respective probability of 1−

p, with p=T(F(v)), into the modified learning set V′

.

3. :

The method according to claim 1, wherein step (b) further comprises;

(b1) forming a score value set Q comprising score values q from the set V by at least one score function F;

V→

Q, VF(V)=q;

(b2) forming a probability set P comprising probabilities p from the score value set Q by at least one transformation function T;

Q→

P, qT(q)=T(F(v))=p; and

(b3) forming the modified learning set V′

from measured values, wherein the measured values v∈

V are included with a respective probability of 1−

p, with p=T(F(v)), into the modified learning set V′

;

wherein the measured values v∈

V are given a respective weighting by at least one weighting function G.

4. :

The method according to claim 2, wherein the method comprises steps (b1) to (b3) in the recited order.

5. :

The method according to claim 1, further comprising;

determining whether the system S is in an error-free or an erroneous state.

6. :

The method according to claim 2, wherein the score function F represents an independent learning system L′ and

rating system B′

with output of a score value.

7. :

The method according to claim 2, wherein the score function F represents an independent machine learning system L′ and

rating system B′

with output of a score value.

8. :

The method according to claim 2, wherein the score function F is formed by considering one or more of the following;

the k next neighbors, the interquartile multiplying factor, the local outlier factor.

9. :

The method according to claim 2, wherein the transformation function T is a continuously increasing function.

10. :

The method according to claim 9, wherein the transformation function T is a continuously increasing function with 0≦

T(x)≦

1 for all x∈

.

11. :

The method according to claim 9, wherein the transformation function T is a normal distribution, a Weibull distribution, a beta distribution or a continuous equipartition.

12. :

The method according to claim 3, wherein the weighting function G is defined as G(p)=1−

p=1−

T(F(v)).

13. :

The method according to any claim 2, wherein steps (b1) to (b3) are carried out several times successively in an iterative manner.

14. :

The method according to claim 1, wherein in step (a) the set V is partitioned in sub-sets V_1, . . . , V_N with N∈

, and

wherein in step (b) modified learning sub-sets V_1′

, . . . , V_N′

with N∈

are formed and the learning set V is combined from the modified learning sub-sets V_1′

, . . . , V_N′

.

16. :

The method according to claim 1, wherein measured values are selected from the group consisting of;

capacity utilization of a calculating unit, used and free storage space, capacity utilization and state of input and output channels, number of error-free or erroneous packets, lengths of transmission queues, error-free and erroneous service inquiries, processing time of a service inquiry.

15. (canceled)

17. :

A system for rating measured values taken from a system S that may be in an error-free or erroneous state, wherein the system S comprises at least one communication network, a network component of a communication system or a service of a communication system, the system comprising a processor and a non-transitory computer-readable medium having processor-executable instructions stored thereon, wherein execution of the processor-executable instructions by the processor facilitates the following;

forming a set Y of unmarked measured values v from the system S;

forming a modified learning set V′

comprising measured values v′

for a learning system L by (I) removal or (h) weighting or (in) removal and weighing of measured values from the set V using a random-based method;

forming a model M for rating measured values from the system S from the modified learning set V′

; and

rating measured values from the system S using the model M.

18. :

The system according to claim 17, wherein forming the modified learning set V′

further comprises;

forming a score value set Q comprising score values q from the set V by at least one score function F;

V→

Q, vF(v)=q; and

forming a probability set P with probabilities p from the score value set Q by at least one transformation function T;

Q→

P, qT(q)=T(F(v))=p;

wherein forming the modified learning set V′

further comprises forming the modified learning set V′

of measured values by introducing the measured values v∈

V with a corresponding probability of 1−

p, with p−

T(F(v)) into the modified learning set V′ and

by weighting the measured values v∈

V by at least one weighting function G; and

wherein forming the modified learning set V′

further comprises removing at least one closest neighbor of the measured value v from the set V during (i) removal, or (ii) weighting or (iii) removal and weighting of measured values v.

19. :

The system according to claim 17, wherein forming the modified learning set V′

further comprises;

forming a score value set Q comprising score values q from the set V by at least one score function F;

V→

Q, vF(v)=q; and

forming a probability set P with probabilities p from the score value set Q by at least one transformation function T;

Q→

P, qT(q)=T(F(v))=p; and

wherein forming the modified learning set V′

further comprises forming the modified learning set V′

of measured values by introducing the measured values v∈

V with a corresponding probability of 1−

p, with p=T(F(v)) into the modified learning set V′ and

by weighting the measured values v∈

V by at least one weighting function G.

20. :

The system according to claim 17, wherein execution of the processor-executable instructions by the processor further facilitates;

determining whether the system S is in an error-free or erroneous state.

21. :

The system according to claim 18, wherein execution of the processor-executable instructions by the processor further facilitates;

forming the score value set Q several times; and

forming the probability set P_several times; and

forming the modified learning set V′

several times.

22. :

The system according to claim 17, wherein forming the set V of unmarked measured values v from the system S further comprises partitioning the set V into sub-sets V_1, . . . , V_N with N∈

, and

wherein forming the modified learning set V′

further comprises forming modified learning sub-sets V_1′

, . . . , V_N′

with N∈

.

24. :

The system according to claim 17, wherein measured values are selected from the group consisting of;

capacity utilization of a calculating unit, used and free storage space, capacity utilization and state of input and output channels, number of error-free or erroneous packets, lengths of transmission queues, error-free and erroneous service inquiries, processing time of a service inquiry.

23. (canceled)