System and method for analyzing network performance data

Patent metadata

Field	Value
Publication number	US-10952082-B2
Application number	US-201716474130-A
Country	US
Kind code	B2
Filing date	Jan 26, 2017
Priority date	Jan 26, 2017
Publication date	Mar 16, 2021
Grant date	Mar 16, 2021

Abstract

A computer system and computer-automated method for analyzing performance data in a telecommunications network. A data set is provided that contains a log of a first time sequence of network events which are classified into event types. A second time sequence is generated from the first time sequence by aggregating the events into event groups, and at least a third time sequence is generated by aggregating the event groups into event super-groups. A multi-level time sequence event hierarchy of at least three levels is thus created. The multiple time sequence levels are rendered into a visualization in which the different event types are visually distinct from each other. The visualization reveals to a domain expert patterns of behavior in the data set which can be used to detect current network problems and to predict future network loading, for example in a network operations center.

First claim

The invention claimed is:

1. A computer-automated method for analyzing network performance data, the method comprising:
receiving a data set containing a log of a first time sequence of network events in which each network event is associated with at least one network location and has been classified into one of a plurality of event types;
creating a second time sequence from the first time sequence by aggregating the events into event groups, wherein each event group is defined as a plurality of events which are in a specific sequence of event types, each event group being classified into one of a plurality of event group types;
creating a third time sequence from the second time sequence by aggregating the event groups into event super-groups, wherein each event super-group is defined as a plurality of event groups which are in a specific sequence of event group types, each event super-group being classified into one of a plurality of event super-group types;
generating a Graphical User Interface (GUI) to include a visualization of at least one of the time sequences such that in the visualization, each of the types is visually distinct from other types in the same time sequence; and
sending the GUI to a display device for display to a user.

2. The method of claim 1 , wherein the visualization includes a map representation in relation to the network locations and according to at least one of the second time sequence and the third time sequence.

3. The method of claim 1 , further comprising:
recognizing a pattern in the data set by matching the current event groups and/or super-groups to a first time period of at least one stored data set in which the same event groups and/or super-groups are present; and
predicting future network loading based on warping the first time period of the at least one stored data set onto the current data set and using the warped second time period of the at least one stored data set as the prediction.

4. The method of claim 3 , wherein the visualization includes a map representation of the predicted future network loading.

5. The method of claim 2 , wherein the map representation encodes time with color and/or shading in a single image frame, and such that separate image frames relate to specific times or periods of time which can be displayed in time order.

6. The method of claim 2 , further comprising modifying the map representation to filter in and out based on event types, event group types, and/or event super-group types in response to input from user actuatable controls.

7. The method of claim 1 , further comprising modifying the visualization of the first time sequences, the second time sequence, and/or the third time sequence to filter in and out based on event types, event group types, and/or event super-group types in response to input from user actuatable controls.

8. The method of claim 6 , further comprising:
predicting future network loading based on applying the input from the user actuatable controls to filter out at least some of the events contained in the data set; and
saving a modified version of the data set with the events which have been filtered out from the visualization being removed.

9. The method of claim 3 , further comprising:
comparing the predicted future network loading to network capacity to predict any capacity shortfalls; and
provisioning additional network capacity to address any such capacity shortfalls before they are predicted to occur.

10. The method of claim 1 , wherein the location is a geographical location and/or an association with a network entity in a network diagram.

11. The method of claim 1 , wherein the event group types are predefined.

12. The method of claim 1 , wherein the event group types are defined as part of creating the second time sequence from the first time sequence and according to the first time sequence.

13. The method of claim 1 , wherein the event super-group types are predefined.

14. The method of claim 1 , wherein the event super-group types are defined as part of creating the third time sequence from the second time sequence and according to the second time sequence.

15. The method of claim 1 , wherein in each time series each type is ascribed a different visual characteristic for the visualization.

16. The method of claim 1 :
wherein each event is ascribed a value of a quality parameter; and
wherein the visualization represents a range of the quality parameter values by a range of values of a visualization parameter.

17. The method of claim 1 , further comprising creating at least one higher order time sequence from the time sequence of the previous highest order by aggregating the groups of the previous highest order, referred to as sub-ordinate groups, into supra-ordinate groups, wherein each supra-ordinate group is defined as a plurality of sub-ordinate groups which are in a specific sequence of sub-ordinate group types, each supra-ordinate group being classified into one of a plurality of event supra-ordinate group types.

18. A non-transitory computer readable recording medium storing a computer program product for controlling a computing device for automated analyzing of network performance data, the computer program product comprising software instructions which, when run on processing circuitry of the computing device, causes the computing device to:
receive a data set containing a log of a first time sequence of network events in which each network event is associated with at least one network location and has been classified into one of a plurality of event types;
create a second time sequence from the first time sequence by aggregating the events into event groups, wherein each event group is defined as a plurality of events which are in a specific sequence of event types, each event group being classified into one of a plurality of event group types;
create a third time sequence from the second time sequence by aggregating the event groups into event super-groups, wherein each event super-group is defined as a plurality of event groups which are in a specific sequence of event group types, each event super-group being classified into one of a plurality of event super-group types;
generate a Graphical User Interface (GUI) to include a visualization of the time sequences such that in the visualization each of the types is visually distinct from other types in the same time sequence; and
send the GUI to a display device for display to a user.

19. A computer system for analyzing network performance data, the system comprising:
a data input operable to receive a data set containing a log of a first time sequence of network events in which each network event is associated with at least one network location and has been classified into one of a plurality of event types;
memory operable to store the data set;
processing circuitry operable to analyze the data set by:
creating a second time sequence from the first time sequence by aggregating the events into event groups, wherein each event group is defined as a plurality of events which are in a specific sequence of event types, each event group being classified into one of a plurality of event group types;
creating a third time sequence from the second time sequence by aggregating the event groups into event super-groups, wherein each event super-group is defined as a plurality of event groups which are in a specific sequence of event group types, each event super-group being classified into one of a plurality of event super-group types;
generating a Graphical

Assignees

• Ericsson Telefon Ab L M

Inventors

• Gódor István
• Fenyvesi Nóra
• Kallus Zsófia
• Stéger József

Classifications

• H04L41/149

  for prediction of maintenance · CPC title

• H04L41/147 Primary

  for predicting network behaviour · CPC title

• H04W16/22 Primary

  Traffic simulation tools or models · CPC title

• G06N20/00

  Machine learning · CPC title

• H04L43/0876

  Network utilisation, e.g. volume of load or congestion level · CPC title