Intelligent management of a synchronization interval for data of an application or service
The present disclosure relates to processing operations configured for intelligent management of a synchronization interval usable to synchronize data for an application/service between local data storage and distributed data storage. A classification prediction for a synchronization interval may be generated that is used as prediction for modifying the synchronization interval when a plurality of computing devices, linked to one or more user accounts, are used to access content. For instance, a suggestion is made as to whether a synchronization interval should be made faster, slower or even remain unchanged when a user account is accessing content. This may occur based on analysis of contextual factors described herein, thereby enabling a synchronization prediction component to make a best possible prediction for setting a synchronization interval. Artificial intelligence processing may be trained to generate the classification prediction for a synchronization interval. The classification prediction is utilized to manage modification of synchronization interval.
1. A method comprising:
analyzing historical device usage data of a user account indicating prior access to content of an application or service through a plurality of client devices linked to the user account;
determining, based on a result of the analyzing of the historical device usage data of the user account, whether two or more of the plurality of client devices linked to the user account accessed the content over a predetermined time period and whether the two or more of the plurality of client devices modified the content during the predetermined time period;
generating a synchronization interval classification prediction for the user account based on a result of the determining, wherein generating the synchronization interval classification prediction comprises applying trained artificial intelligence processing configured to generate the synchronization interval classification prediction based on a correlation result of correlating data indicating whether the two or more of the plurality of client devices accessed the content during the predetermined time period with data indicating whether the two or more of the plurality of client devices modified the content during the predetermined time period, and wherein the synchronization interval classification prediction is usable to predict a synchronization interval for synchronizing the content with a distributed data storage associated with the application or service; and
transmitting the synchronization interval classification prediction to a synchronization component of the application or service that is usable to modify a synchronization interval.
2. The method of claim 1 , further comprising: receiving a query, from the synchronization component, usable to modify the synchronization interval, requesting the synchronization interval classification prediction; and in response to receiving the query, applying a trained artificial intelligence processing configured to generate the synchronization interval classification prediction, wherein the trained artificial intelligence processing executes processing operations that comprise the analyzing, the determining, the generating and the transmitting.
3. The method of claim 1 , wherein the synchronization interval classification prediction is a recommendation to adjust the synchronization interval to a shorter interval based on the result of the determining identifying that the two or more of the plurality of client devices linked to the user account accessed the content over the predetermined time period.
4. The method of claim 1 , wherein the synchronization interval classification prediction is a recommendation to adjust the synchronization interval to a longer interval based on the result of the determining identifying that the two or more of the plurality of client devices did not access the content over the predetermined time period.
5. The method of claim 1 , wherein the application or service is a notetaking application or service, and wherein the content is a section in a notebook of the notetaking application or service.
6. The method of claim 5 , wherein the historical device usage data comprises metadata identifying the section in the notebook, and one or more entries providing: an identifier of a device of the plurality of client devices linked to the user account that were used to access the section, and a timestamp for access to the section by the device.
7. The method of claim 1 , wherein the analyzing of the historical device usage data further comprises parsing the historical device usage data of the user account to determine whether any of the plurality of client devices modified the content during the predetermined time period, and wherein the determining whether the two or more of the plurality of client devices accessed the content comprises determining whether the two or more of the plurality of client devices modified the content during the predetermined time period.
8. A system comprising:
at least one processor; and
a memory, operatively connected with the at least one processor, storing computer-executable instructions that, when executed by the at least one processor, causes the at least one processor to execute a method that comprises:
analyzing historical device usage data of a user account indicating prior access to content of an application or service through a plurality of client devices linked to the user account;
determining, based on a result of the analyzing of the historical device usage data of the user account, whether two or more of the plurality of client devices linked to the user account accessed the content over a predetermined time period and whether the two or more of the plurality of client devices modified the content during the predetermined time period;
generating a synchronization interval classification prediction for the user account based on a result of the determining, wherein the generating the synchronization interval classification prediction comprises applying trained artificial intelligence processing configured to generate the synchronization interval classification prediction based on a correlation result of correlating data indicating whether the two or more of the plurality of client devices accessed the content during the predetermined time period with data indicating whether the two or more of the plurality of client devices modified the content during the predetermined time period, and wherein the synchronization interval classification prediction is usable to predict a synchronization interval for synchronizing the content with a distributed data storage associated with the application or service; and
transmitting the synchronization interval classification prediction to a synchronization component of the application or service that is usable to modify a synchronization interval.
9. The system of claim 8 , wherein the method, executed by the at least one processor, further comprises: receiving a query from the synchronization component, usable to modify the synchronization interval, requesting the synchronization interval classification prediction; and in response to receiving the query, applying a trained artificial intelligence processing configured to generate the synchronization interval classification prediction, wherein the trained artificial intelligence processing executes processing operations that comprise the analyzing, the determining, the generating and the transmitting.
10. The system of claim 8 , wherein the synchronization interval classification prediction is a recommendation to adjust the synchronization interval to a shorter interval based on the result of the determining identifying that the two or more of the plurality of client devices linked to the user account accessed the content over the predetermined time period.
11. The system of claim 8 , wherein the synchronization interval classification prediction is a recommendation to adjust the synchronization interval to a longer interval based on the result of the determining identifying that the two or more of the plurality of client devices did not access the content over the predetermined time period.
12. The system of claim 8 , wherein the application or service is a notetaking application or service, and wherein the content is a section in a notebook of the notetaking application or service.
13. The system of claim 12 , wherein the historical device usage data comprises metadata identifying the section in the notebook, and one or more entries providing: an identifier of a device of the plurality of client devices linked to the user account that were used to access the section, and a timestamp for access to the section by the device.
14. The system of claim 8 , wherein the analyzing of the historical device usage data further comprises parsing the historical device usage data of the user account to determine whether any of the plurality of client devices modified the content during the predetermined time period, and wherein the determining whether the two or more of the plurality of client devices accessed the content comprises determining whether the two or more of the plurality of client devices modified the content during the predetermined time period.
15. A method comprising:
analyzing historical device usage data of a user account indicating prior modification of content of an application or service through a plurality of client devices linked to the user account;
determining, based on a result of the analyzing of the historical device usage data of the user account, whether two or more of the plurality of client devices linked to the user account accessed the content over a predetermined time period and whether the two or more of the plurality of client devices linked to the user account modified the content over the predetermined time period;
generating a synchronization interval classification prediction for the user account based on a result of the determining, wherein generating the synchronization interval classification prediction comprises applying trained artificial intelligence processing configured to generate the synchronization interval classification prediction based on a correlation result of correlating data indicating whether the two or more of the plurality of client devices accessed the content during the predetermined time period with data indicating whether the two or more of the plurality of client devices modified the content during the predetermined time period, and wherein the synchronization interval classification prediction is usable to predict a synchronization interval for synchronizing the content with a distributed data storage associated with the application or service; and
modifying a synchronization interval based on the synchronization interval classification prediction.
16. The method of claim 15 , further comprising: synchronizing a locally stored version of the content with a version of the content maintained by the distributed data storage based on the synchronization interval set based on the modifying.
17. The method of claim 15 , further comprising: receiving a query requesting the synchronization interval classification prediction; and in response to receiving the query, applying a trained artificial intelligence processing configured to generate the synchronization interval classification prediction, and wherein the trained artificial intelligence processing executes processing operations that comprise the analyzing, the determining, the generating.
18. The method of claim 15 , further comprising: transmitting the synchronization interval classification prediction to a synchronization component of the application or service, and wherein the synchronization component executes the modifying of the synchronization interval.