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US-20260127489-A1 - MONITORING DATA EVENTS FOR UPDATING MODEL

US20260127489A1US 20260127489 A1US20260127489 A1US 20260127489A1US-20260127489-A1

Abstract

Example embodiments of the present disclosure relate to model updating. A device obtains a configuration indicating a data event for data associated with a model to be monitored. The data event indicates: a parameter to be monitored and a threshold associated with the parameter. The device triggers monitoring of the parameter and detects whether a value of the parameter fulfills a condition indicated in the configuration while monitoring the parameter. In this way, the training of the model is enhanced to handle data events.

Inventors

  • Sivaramakrishnan Swaminathan
  • Stephen MWANJE
  • Shuqiang SUN

Assignees

  • NOKIA TECHNOLOGIES OY

Dates

Publication Date
20260507
Application Date
20220805

Claims (20)

  1. 1 . A device comprising: at least one processor; and at least one memory storing instructions that, when executed by the at least one processor, cause the device at least to perform operations, the operations comprising: obtaining a configuration indicating a data event associated with a model, wherein the data event is to be monitored to determine whether to retrain the model, the data event comprises: a parameter; and a threshold associated with the parameter, wherein the parameter comprises a performance measurement or a key performance indicator; triggering monitoring of the data event; and based on detecting that a value of the parameter satisfies the threshold associated with the parameter, retraining the model or triggering retraining of the model.
  2. 2 . The device of claim 1 , wherein the obtaining the configuration indicating the data event comprises: receiving from another device the configuration indicating the data event.
  3. 3 - 6 . (canceled)
  4. 7 . The device claim 1 , wherein the data event further comprises at least one of: a threshold direction; or a hysteresis value.
  5. 8 . The device of claim 1 , wherein the operations further comprise: transmitting to another device a retraining result of the retraining the model.
  6. 9 . The device of claim 7 , wherein the data event comprises the threshold direction and wherein the detecting a that the value of the parameter satisfies the threshold associated with the parameter comprises: determining that the value of the parameter crossed the threshold in the threshold direction.
  7. 10 . The device of claim 9 , wherein the data event further comprises the hysteresis value and wherein the determining that the value of the parameter crossed the threshold in the threshold direction considering the hysteresis value.
  8. 11 . The device of claim 1 , wherein the device comprises an artificial intelligent machine learning producer with artificial intelligent machine learning monitoring function.
  9. 12 - 16 . (canceled)
  10. 17 . A method comprising: obtaining, at a device, a configuration indicating a data event associated with a model, wherein the data event is to be monitored to determine whether to retrain the model, the data event comprising: a parameter; and a threshold associated with the parameter, wherein the parameter comprises a performance measurement of the model or a key performance indicator of the model; triggering, at the device, monitoring of the data event; and based on detecting that a value of the parameter satisfies the threshold associated with the parameter, retraining the model or triggering retraining of the model.
  11. 18 . The method of claim 17 , wherein the obtaining the configuration indicating the data event comprises: receiving from another device the configuration indicating the data event.
  12. 19 - 22 . (canceled)
  13. 23 . The method of claim 17 , wherein the data event further comprises at least one of: a threshold direction; or a hysteresis value.
  14. 24 . The method of claim 17 , further comprising: transmitting to another device a retraining result of the retraining the model.
  15. 25 . The method of claim 23 , wherein the data event comprises the threshold direction and wherein the detecting that a value of the parameter satisfies the threshold associated with the parameter comprises: determining that the value of the parameter crossed the threshold in the threshold direction.
  16. 26 . The method of claim 25 , wherein the data event further comprises the hysteresis value, and data event comprises the hysteresis value and wherein the determining that the value of the parameter crossed the threshold in the threshold direction considering the hysteresis value.
  17. 27 . The method of claim 17 , wherein the device comprises an artificial intelligent machine learning producer with artificial intelligent machine learning monitoring function.
  18. 28 - 34 . (canceled)
  19. 35 . A non-transitory computer readable medium comprising instructions stored thereon which, when executed by at least one processor of a device cause the device to perform: obtaining, a configuration indicating a data event associated with a model, wherein the data event is to be monitored to determine whether to retrain the model, the data event comprising: a parameter; and a threshold associated with the parameter, wherein the parameter comprises a performance measurement of the model or a key performance indicator of the model; triggering a monitoring of the data event; and based on detecting that a value of the parameter satisfies the threshold associated with the parameter, retraining the model or triggering retraining of the model.
  20. 36 . The non-transitory computer readable medium of claim 35 , wherein the obtaining the configuration indicating the data event comprises: receiving from another device the configuration indicating the data event.

Description

FIELD Various example embodiments of the present disclosure generally relate to the field of telecommunications and in particular, to methods, devices, apparatuses and computer readable storage medium for updating models employed in a telecommunication system. BACKGROUND In the telecommunication industry, technologies have been proposed to improve performance of telecommunication systems. For example, Artificial Intelligence/Machine Learning (AI/ML) models have been employed in telecommunication systems to improve the performance of telecommunications systems. Therefore, it is worthy studying on how to and when to update the AI/ML models employed in a telecommunication system. SUMMARY In a first aspect of the present disclosure, there is provided a device. The device comprises at least one processor; and at least one memory storing instructions that, when executed by the at least one processor, cause the device at least to perform: obtaining a configuration indicating a data event for data associated with a model to be monitored, the data event indicating: a parameter to be monitored and a threshold associated with the parameter; triggering monitoring of the parameter based on the configuration indicating the data event; and detecting whether a value of the parameter fulfills a condition indicated in the configuration while monitoring the parameter. In a second aspect of the present disclosure, there is provided a device. The device comprises at least one processor; and at least one memory storing instructions that, when executed by the at least one processor, cause the device at least to perform: transmitting, to another device, a configuration indicating a data event for data associated with a model to be monitored, the data event indicating: a parameter to be monitored and a threshold associated with the parameter. In a third aspect of the present disclosure, there is provided a method. The method comprises: obtaining, at a device, a configuration indicating a data event for data associated with a model to be monitored, the data event indicating: a parameter to be monitored and a threshold associated with the parameter; triggering monitoring of the parameter based on the configuration indicating the data event; and detecting whether a value of the parameter fulfills a condition indicated in the configuration while monitoring the parameter. In a fourth aspect of the present disclosure, there is provided a method. The method comprises: transmitting, at a device and to another device, a configuration indicating a data event for data associated with a model to be monitored, the data event indicating: a parameter to be monitored and a threshold associated with the parameter. In a fifth aspect of the present disclosure, there is provided an apparatus. The apparatus comprises: means for obtaining a configuration indicating a data event for data associated with a model to be monitored, the data event indicating: a parameter to be monitored and a threshold associated with the parameter; means for triggering monitoring of the parameter based on the configuration indicating the data event; and means for detecting whether a value of the parameter fulfills a condition indicated in the configuration while monitoring the parameter. In a sixth aspect of the present disclosure, there is provided an apparatus. The apparatus comprises means for transmitting, to another apparatus, a configuration indicating a data event for data associated with a model to be monitored, the data event indicating: a parameter to be monitored and a threshold associated with the parameter. In a seventh aspect of the present disclosure, there is provided a computer readable medium. The computer readable medium comprises instructions stored thereon for causing an apparatus to perform at least the method according to the third aspect. In an eighth aspect of the present disclosure, there is provided a computer readable medium. The computer readable medium comprises instructions stored thereon for causing an apparatus to perform at least the method according to the fourth aspect. It is to be understood that the Summary section is not intended to identify key or essential features of embodiments of the present disclosure, nor is it intended to be used to limit the scope of the present disclosure. Other features of the present disclosure will become easily comprehensible through the following description. BRIEF DESCRIPTION OF THE DRAWINGS Some example embodiments will now be described with reference to the accompanying drawings, where: FIG. 1A and FIG. 1B illustrate example communication environments in which example embodiments of the present disclosure can be implemented; FIG. 2A and FIG. 2B illustrate signaling diagrams for communication according to some example embodiments of the present disclosure; FIG. 3 illustrates a signaling diagram for communication according to some example embodiments of the present disclosure; FIG. 4 illustrates a signal