Predictive scoring based on key performance indicators in telecommunications system
A method includes: receiving protocol event data from a plurality of probes within the telecommunication system; determining a most probable cause of a call event from the protocol event data; applying the most probable cause to a trained machine learning algorithm that includes the most probable cause as its input and a telecommunication system score as its output; and in response to an output score from the trained machine learning algorithm, performing a corrective action for a plurality of network users that are expected to be affected by the most probable cause.
1. A non-transitory machine readable medium having stored thereon instructions for performing a method comprising machine executable code which when executed by at least one machine, causes the machine to:
receive protocol event data from a plurality of probes within a telecommunication system;
determine a multi-protocol Key Performance Indicator (KPI) of a call event from the protocol event data;
apply the multi-protocol KPI to a trained machine learning algorithm that includes the multi-protocol KPI as the trained machine learning algorithm's input and a telecommunication system score as the trained machine learning algorithm's output; and
in response to an output score from the trained machine learning algorithm, perform a corrective action for a plurality of network users that are expected to be affected by the multi- protocol KPI, the corrective action including determining a most probable cause for the multi- protocol KPI, using the most probable cause as a key to search through call records to identify the plurality of network users, and sending a short message service (SMS) message to the plurality of network users, the SMS message indicating the most probable cause or an action to take.
2. The non-transitory machine readable medium of claim 1 , further comprising code to cause the machine to send a survey to a subset of the plurality of network users and verifying the output score using returned survey results.
3. The non-transitory machine readable medium of claim 1 , further comprising code to cause the machine to send a survey to a subset of the plurality of network users and tuning the trained machine learning algorithm using returned survey results.
4. The non-transitory machine readable medium of claim 1 , wherein the plurality of probes collect data transmitted between a Radio Access Network (RAN) and a Mobility Management Element (MME).
5. The non-transitory machine readable medium of claim 1 , wherein the plurality of probes collect data transmitted between a Radio Access Network (RAN) and a Serving Gateway (SGW).
6. The non-transitory machine readable medium of claim 1 , wherein the protocol event data includes data relating to a multitude of different protocol processes for a plurality of different protocols from call set up through call termination for a plurality of calls made in the telecommunication system.
7. The non-transitory machine readable medium of claim 1 , wherein multi-protocol KPI indicates a plurality of different cause codes in a plurality of different protocols.
8. The non-transitory machine readable medium of claim 1 , wherein determining the multi-protocol KPI includes:
feeding the protocol event data to an additional trained machine learning algorithm that is configured to output the multi-protocol KPI.
9. The non-transitory machine readable medium of claim 1 , wherein the multi-protocol KPI is associated with a call failure, and anomaly, or call degradation.
10. A method comprising:
receiving protocol event data from a plurality of probes within a telecommunication system;
determining a multi-protocol Key Performance Indicator (KPI) of a call event from the protocol event data;
applying the multi-protocol KPI to a trained machine learning algorithm that includes the multi-protocol KPI as the trained machine learning algorithm's input and a telecommunication system score as the trained machine learning algorithm's output; and
in response to an output score from the trained machine learning algorithm, performing a corrective action for a plurality of network users that are expected to be affected by the multi-protocol KPI, the corrective action including determining a most probable cause for the multi- protocol KPI, using the most probable cause as a key to search through call records to identify the plurality of network users, and sending a short message service (SMS) message to the plurality of network users, the SMS message indicating the most probable cause or an action to take.
11. The method of claim 10 , further comprising sending a survey to a subset of the plurality of network users and verifying the output score using returned survey results.
12. The method of claim 10 , further comprising sending a survey to a subset of the plurality of network users and tuning the trained machine learning algorithm using returned survey results.
13. The method of claim 10 , wherein the plurality of probes collect data transmitted between a Radio Access Network (RAN) and a Mobility Management Element (MME).
14. The method of claim 10 , wherein the plurality of probes collect data transmitted between a Radio Access Network (RAN) and a Serving Gateway (SGW).
15. The method of claim 10 , wherein the protocol event data includes data relating to a multitude of different protocol processes for a plurality of different protocols from call set up through call termination for a plurality of calls made in the telecommunication system.
16. The method of claim 10 , wherein multi-protocol KPI indicates a plurality of different cause codes in a plurality of different protocols.
17. The method of claim 10 , wherein determining the multi-protocol KPI includes:
feeding the protocol event data to an additional trained machine learning algorithm that is configured to output the multi-protocol KPI.
18. The method of claim 10 , wherein the multi-protocol KPI is associated with a call failure, and anomaly, or call degradation.