Alerting system for software applications

1. A non-transitory machine-readable medium storing a program executable by at least one processing unit of a device, the program comprising sets of instructions for customizing a subset of visualizations for a particular user using a machine-learning based predictive model trained on user feedback:

displaying a set of visualizations to a user on a user interface of a client device, the set of visualizations associated with the user of the client device;

calculating a score for each visualization in the set of visualizations, wherein the score is based on a plurality of criteria,

said plurality of criteria comprising: a count of a same key performance indicator (KPI) in other visualizations and other visualizations to which the user is subscribed;

wherein calculating the score comprises calculating a weighted sum of the scores for each criteria, wherein a weight for each criteria is based on the machine-learning based predictive model trained on user feedback;

determining the subset of the set of visualizations corresponding to a plurality of highest scores; and

displaying notifications to the user on the user interface of the client device for the subset, the notifications indicating the data associated with the subset of the set of visualizations has changed.

2. The non-transitory machine-readable medium of claim 1 , wherein the program further comprises sets of instructions for:

determining the particular visualization based on a history of interactions with visualizations performed by the user, wherein the user is not subscribed to the particular visualization; and

providing the client device with a notification indicating the particular visualization as a suggestion for subscribing.

3. The non-transitory machine-readable medium of claim 2 , wherein determining the history of interactions comprises:

determining a number of views of the particular visualization by the user that is greater than a defined threshold number of views.

4. The non-transitory machine-readable medium of claim 2 , wherein the particular visualization is a first visualization, and wherein determining the first visualization comprises:

determining the history of interactions comprises:

determining interactions with a second visualization; and

determining at least one entity of data used by the first visualization is the same as an entity of data used by the second visualization.

5. The non-transitory machine-readable medium of claim 2 , wherein the particular visualization is a first visualization, and wherein determining the first visualization comprises:

determining the history of interactions comprises:

determining interactions with a second visualization; and

determining that a title of the first visualization is similar to a title of the second visualization.

6. The non-transitory machine-readable medium of claim 2 , wherein determining the particular visualization comprises:

determining a set of users who are similar to the user; and

determining the particular visualization is subscribed to by at least one user in the set of users.

7. The non-transitory machine-readable medium of claim 1 , wherein displaying the set of visualizations further comprises:

providing suggestions to the user of visualizations to subscribe to based on a model-based technique, a memory-based technique, or a text-embedding based technique.

8. A method for customizing a subset of visualizations for a particular user using a machine-learning based predictive model trained on user feedback comprising:

displaying a set of visualizations to a user on a user interface of a client device, the set of visualizations associated with the user of the client device;

calculating a score for each visualization in the set of visualizations, wherein the score is based on a plurality of criteria,

said plurality of criteria comprising: a count of a same key performance indicator (KPI) in other visualizations and other visualizations to which the user is subscribed,

wherein calculating the score comprises calculating a weighted sum of the scores for each criteria, wherein a weight for each criteria is based on the machine-learning based predictive model trained on user feedback;

determining the subset of the set of visualizations corresponding to a plurality of highest scores; and

displaying notifications to the user on the user interface of the client device for the subset, the notifications indicating the data associated with the subset of the set of visualizations has changed.

9. The method of claim 8 further comprising:

determining the particular visualization based on a history of interactions with visualizations performed by the user, wherein the user is not subscribed to the particular visualization; and

providing a notification to a client device indicating the particular visualization as a suggestion for subscribing.

10. The method of claim 9 , wherein determining the history of interactions comprising:

determining a number of views of the particular visualization by the user that is greater than a defined threshold number of views.

11. The method of claim 9 , wherein the particular visualization is a first visualization, and wherein determining the first visualization comprises:

determining the history of interactions comprising:

determining interactions with a second visualization; and

determining at least one entity of data used by the first visualization is the same as an entity of data used by the second visualization.

12. The method of claim 9 , wherein the particular visualization is a first visualization, and wherein determining the first visualization comprises:

determining the history of interactions comprising:

determining interactions with a second visualization; and

determining that a title of the first visualization is similar to a title of the second visualization.

13. The method of claim 9 , wherein determining the particular visualization comprises:

determining a set of users who are similar to the user; and

determining the particular visualization is subscribed to by at least one user in the set of users.

14. The method of claim 8 , wherein displaying the set of visualizations further comprises:

providing suggestions to the user of visualizations to subscribe to based on a model-based technique, a memory-based technique, or a text-embedding based technique.

15. A system for customizing a subset of visualizations for a particular user using a machine-learning based predictive model trained on user feedback comprising:

a set of processing units; and

a non-transitory machine-readable medium storing instructions that when executed by at least one processing unit in the set of processing units cause the at least one processing unit to:

display a set of visualizations to a user on a user interface of a client device, the set of visualizations associated with the user of the client device;

calculate a score for each visualization in the set of visualizations, wherein the score is based on a plurality of criteria,

said plurality of criteria comprising: a count of a same key performance indicator (KPI) in other visualizations and other visualizations to which the user is subscribed;

wherein calculating the score comprises calculating a weighted sum of the scores for each criteria, wherein a weight for each criteria is based on the machine-learning based predictive model trained on user feedback;

determine the subset of the set of visualizations corresponding to a plurality of highest scores; and

display notifications to the user on the user interface of the client device for the subset, the notifications indicating the data associated with the subset of the set of visualizations has changed.

16. The system of claim 15 , wherein the instructions further cause the at least one processing unit to:

determine the particular visualization based on a history of interactions with visualizations performed by the user, wherein the user is not subscribed to the particular visualization; and

provide to a client device a notification indicating the particular visualization as a suggestion for subscribing.

17. The system of claim 16 , wherein determining the history of interactions comprises:

determining a number of views of the particular visualization by the user that is greater than a defined threshold number of views.

18. The system of claim 16 , wherein the particular visualization is a first visualization, and wherein determining the first visualization comprises:

determining the history of interactions comprising:

determining interactions with a second visualization; and

determining at least one entity of data used by the first visualization is the same as an entity of data used by the second visualization.

19. The system of claim 16 , wherein the particular visualization is a first visualization, and wherein determining the first visualization comprises:

determining the history of interactions comprising:

determining interactions with a second visualization; and

determining that a title of the first visualization is similar to a title of the second visualization.

20. The system of claim 15 , wherein displaying the set of visualizations further comprises:

providing suggestions to the user of visualizations to subscribe to based on a model-based technique, a memory-based technique, or a text-embedding based technique.