Predicting and resolving issues within a telecommunication network

1. A method for resolving a given issue associated with a wireless telecommunication network comprising: (1) obtaining from a database an historical record of prior issues comprising multiple issue tickets and multiple issue ticket resolutions related to multiple components in the wireless telecommunication network; (2) obtaining performance data associated with the wireless telecommunication network; (3) based on the historical record of prior issues, determining a prior issue that is similar to the given issue represented by the performance data associated with the wireless telecommunication network, wherein the determining includes: (A) using a machine-learning model to classify the given issue, thereby resulting in an issue classification, (B) categorizing a type of anomaly associated with the given issue into a predetermined category including a linear anomaly, a sinusoidal anomaly, or a polynomial anomaly, (C) based on the type of the anomaly, performing a regression analysis to fit a first curve to the performance data associated with the wireless telecommunication network, wherein the first curve represents the anomaly associated with the given issue, wherein the regression analysis includes one or more of a linear regression analysis, a sinusoidal regression analysis, a polynomial regression analysis, or a logistic regression analysis, (D) providing a second curve representing an anomaly associated with the prior issue, wherein the prior issue has the same classification as the given issue, wherein the anomaly associated with the prior issue has the same predetermined category as the anomaly associated with the given issue, and wherein the second curve most closely matches the first curve among multiple curves belonging to the predetermined category; (E) based on a difference between the first curve and the second curve, determining the prior issue and the given issue to be similar, wherein the difference between the first curve and the second curve is determined based on an alignment between the first curve and the second curve; (3) determining a solution to the prior issue based at least in part on the classification; and (4) applying the solution to the given issue associated with the wireless telecommunication network.

2. The method of claim 1 , comprising:

obtaining a service registry representing a dependency between the multiple components in the wireless telecommunication network, and a log of a component of the wireless telecommunication network,

wherein the log comprises multiple events including an informational event, a warning, and an error;

based on the service registry, determining a subset of the multiple components dependent on the component of the wireless telecommunication network; and

notifying the subset of the multiple components of the given issue associated with the component.

3. The method of claim 1 , comprising:

determining that the given issue is likely caused by a component of the wireless telecommunication network by:

identifying in the historical record the prior issue among the multiple issue tickets most similar to the given issue;

obtaining from the database information whether the prior issue was caused by a device associated with a user of the wireless telecommunication network; and

upon determining that the prior issue was not caused by the device associated with the user of the wireless telecommunication network, determining that the given issue is likely caused by the component of the wireless telecommunication network.

4. The method of claim 1 , wherein determining the prior issue similar to the given issue further comprises:

training the machine-learning model to identify similar issues based on the multiple issue tickets and the multiple issue ticket resolutions; and

based on the given issue, using the machine-learning model to identify the prior issue similar to the given issue.

5. The method of claim 1 , wherein determining the prior issue similar to the given issue further comprises:

identifying a first qualitative issue associated with the given issue and a second qualitative issue associated with the prior issue,

obtaining a first quantitative amount associated with the first qualitative issue and a second quantitative amount associated with the second qualitative issue; and

determining that the prior issue is similar to the given issue when the first quantitative amount and the second quantitative amount are within a predetermined range of each other.

6. A system for resolving an issue associated with a wireless telecommunication network comprising: one or more processors; memory coupled to the one or more processors, wherein the memory includes instructions executable by the one or more processors to: (1) obtain from a database a record of prior issues comprising multiple issue tickets and multiple issue ticket resolutions related to multiple components in the wireless telecommunication network; (2) obtain performance data associated with the wireless telecommunication network; (3) based on the record of prior issues determine a prior issue similar to the issue represented by the performance data associated with the wireless telecommunication network, wherein the instructions to determine include instructions to: (A) classify the issue to produce a classification, (B) categorize a type of anomaly associated with the issue into a predetermined category including a linear anomaly, a sinusoidal anomaly, or a polynomial anomaly, (C) based on the type of the anomaly, perform a regression analysis, to fit a first curve to the performance data associated with the wireless telecommunication network, wherein the first curve represents the anomaly associated with the issue, wherein the regression analysis includes including a linear regression analysis, a sinusoidal regression analysis, a polynomial regression analysis, or a logistic regression analysis, (D) obtain a second curve representing an anomaly associated with the prior issue, wherein the prior issue has the same classification as the issue, wherein the anomaly associated with the prior issue has the same predetermined category as the anomaly associated with the issue, and wherein the second curve most closely matches the first curve among multiple curves belonging to the predetermined category; (E) based on a difference between the first curve and the second curve, determine the prior issue and the issue to be similar wherein the difference between the first curve and the second curve is determined based on an alignment between the first curve and the second curve; (3) obtain a solution to the prior issue based at least in part on the classification; and (4) apply the solution to the issue associated with the wireless telecommunication network.

7. The system of claim 6 , comprising instructions to:

obtain a service registry representing a dependency between multiple components in the wireless telecommunication network, and a log of a component of the wireless telecommunication network,

wherein the log comprises multiple events including an informational event, a warning, and an error;

based on the service registry, determine a subset of the multiple components dependent on the component of the wireless telecommunication network; and

notify the subset of the multiple components of the issue associated with the component.

8. The system of claim 7 , further comprising instructions to:

detect a security vulnerability by detecting an action on behalf of a user at a first location when the user is at a second location, wherein the first location and the second location are different from each other; and

notify, using the service registry, the user and the component of the wireless telecommunication network affected by the security vulnerability.

9. The system of claim 7 , further comprising instructions to:

analyze an issue ticket among the multiple issue tickets and a corresponding issue ticket resolution among the multiple issue ticket resolutions to determine a subset of related issue tickets and a subset of components affected by the subset of related issue tickets; and

create the service registry by creating the dependency among the subset of components based on the subset of related issue tickets and the subset of components affected by the subset of related issue tickets.

10. The system of claim 7 , further comprising instructions to:

determine that the issue is likely caused by a component of the wireless telecommunication network by:

obtaining information about multiple devices associated with multiple users of the wireless telecommunication network, wherein the multiple devices are in communication with the component of the wireless telecommunication network;

determining a number of devices among the multiple devices related to the error in the log; and

when the number of devices exceeds a predetermined threshold, determining that the issue is likely caused by the component of the wireless telecommunication network.

11. The system of claim 7 , further comprising instructions to:

determine that the issue is likely caused by a component of the wireless telecommunication network by:

identifying in the record the prior issue among the multiple issue tickets most similar to the issue;

obtaining from the database information whether the prior issue was caused by a device associated with a user of the wireless telecommunication network; and

upon determining that the prior issue was not caused by the device associated with the user of the wireless telecommunication network, determining that the issue is likely caused by the component of the wireless telecommunication network.

12. The system of claim 6 , wherein the instructions to determine the prior issue similar to the issue further comprise instructions to:

train a machine-learning model to identify similar issues based on the multiple issue tickets and the multiple issue ticket resolutions; and

based on the issue, use the machine-learning model to identify the prior issue similar to the issue.

13. The system of claim 6 , wherein the instructions to determine the prior issue similar to the issue further comprise instructions to:

identify a first qualitative issue associated with the issue and a second qualitative issue associated with the prior issue,

obtain a first quantitative amount associated with the first qualitative issue and a second quantitative amount associated with the second qualitative issue; and

determine that the prior issue is similar to the issue when the first quantitative amount and the second quantitative amount are within a predetermined range of each other.

14. At least one non-transitory, computer-readable medium, carrying instructions for resolving an issue associated with a wireless telecommunication network that, when executed by at least one data processor, perform a method comprising: (1) obtain from a database a record of prior issues comprising multiple issue tickets and multiple issue ticket resolutions related to multiple components in the wireless telecommunication network; (2) obtain performance data associated with the wireless telecommunication network; (3) based on the record of prior issues, determine a prior issue similar to the issue represented by the performance data associated with the wireless telecommunication network, wherein the instructions to determine include instructions to: (A) classify the issue to produce a classification, (B) categorize a type of anomaly associated with the issue into a predetermined category including a linear anomaly, a sinusoidal anomaly, or a polynomial anomaly, (C) based on the type of the anomaly, perform a regression analysis, to fit a first curve to the performance data associated with the wireless telecommunication network, wherein the first curve represents the anomaly associated with the issue, wherein the regression analysis includes a linear regression analysis, a sinusoidal regression analysis, a polynomial regression analysis, or a logistic regression analysis, (D) obtain a second curve representing an anomaly associated with the prior issue, wherein the prior issue has the same classification as the issue, wherein the anomaly associated with the prior issue has the same predetermined category as the anomaly associated with the issue, and wherein the second curve most closely matches the first curve among multiple curves belonging to the predetermined category; (E) based on a difference between the first curve and the second curve, determine the prior issue and the issue to be similar, wherein the difference between the first curve and the second curve is determined based on an alignment between the first curve and the second curve; (3) obtain a solution to the prior issue based at least in part on the classification; and (4) apply the solution to the issue associated with the wireless telecommunication network.

15. The at least one non-transitory, computer-readable medium of claim 14 , comprising instructions to:

obtain a service registry representing a dependency between the multiple components in the wireless telecommunication network, and a log of a component of the wireless telecommunication network,

wherein the log comprises multiple events including an informational event, a warning, and an error;

based on the service registry, determine a subset of the multiple components dependent on the component of the wireless telecommunication network; and

notify the subset of the multiple components of the issue associated with the component.

16. The at least one non-transitory, computer-readable medium of claim 15 , further comprising instructions to:

analyze an issue ticket among the multiple issue tickets and a corresponding issue ticket resolution among the multiple issue ticket resolutions to determine a subset of related issue tickets and a subset of components affected by the subset of related issue tickets; and

create the service registry by creating the dependency among the subset of components based on the subset of related issue tickets and the subset of components affected by the subset of related issue tickets.

17. The at least one non-transitory, computer-readable medium of claim 15 , further comprising instructions to:

determine that the issue is likely caused by a component of the wireless telecommunication network by:

obtaining information about multiple devices associated with multiple users of the wireless telecommunication network, wherein the multiple devices are in communication with the component of the wireless telecommunication network;

determining a number of devices among the multiple devices related to the error in the log; and

when the number of devices exceeds a predetermined threshold, determining that the issue is likely caused by the component of the wireless telecommunication network.

18. The at least one non-transitory, computer-readable medium of claim 15 , further comprising instructions to:

determine that the issue is likely caused by a component of the wireless telecommunication network by:

identifying in the record the prior issue among the multiple issue tickets most similar to the issue;

obtaining from the database information whether the prior issue was caused by a device associated with a user of the wireless telecommunication network; and

upon determining that the prior issue was not caused by the device associated with the user of the wireless telecommunication network, determining that the issue is likely caused by the component of the wireless telecommunication network.

19. The at least one non-transitory, computer-readable medium of claim 14 , wherein the instructions to determine the prior issue similar to the issue further comprise instructions to:

train a machine-learning model to identify similar issues based on the multiple issue tickets and the multiple issue ticket resolutions; and

based on the issue, use the machine-learning model to identify the prior issue similar to the issue.

20. The at least one non-transitory, computer-readable medium of claim 14 , wherein the instructions to determine the prior issue similar to the issue further comprise instructions to:

identify a first qualitative issue associated with the issue and a second qualitative issue associated with the prior issue,

obtain a first quantitative amount associated with the first qualitative issue and a second quantitative amount associated with the second qualitative issue; and

determine that the prior issue is similar to the issue when the first quantitative amount and the second quantitative amount are within a predetermined range of each other.