Systems and methods for generating, analyzing, and storing data snippets

1. A system comprising:

one or more processors; and

a memory storing instructions that, when executed by the one or more processors, cause the system to perform:

obtaining a data file from a data source through a plug-in interface;

playing the data file for a user through an application running on a computing device;

identifying one or more entities depicted in the data file;

obtaining geospatial information relating to the identified entities from a data platform;

overlaying, through the application, the obtained geospatial information during playback of the data file;

receiving an indication from the user, through the application, to tag an entity depicted in the data file during playback of the data file;

clustering the snippet with one or more other snippets depicting the entity; and

storing, to a data store in accordance with a data type of the snippet, the snippet and the one or more other snippets according to the clustering, wherein the snippet is a portion of the data file corresponding to the entity tagged by the user.

2. The system of claim 1 , wherein the data file is at least one of a video feed, audio clip, or image.

3. The system of claim 1 , wherein the instructions, when executed, cause the system to further perform:

providing a histogram, through the application, synchronously, during playback of the data file.

4. The system of claim 3 , wherein the histogram comprises one or more bars, each bar representing a number of entities depicted in the data file in a given time period.

5. The system of claim 1 , wherein storing a snippet of the data file comprises:

providing an option for the user to add annotations to the snippet; and

saving the annotations as part of metadata information of the snippet.

6. The system of claim 5 , wherein the metadata information of the snippet further includes timestamp information, location information, and timeline information associated with the data file.

7. The system of claim 1 , wherein the portion of the data file corresponding to the entity tagged is user adjustable.

8. The system of claim 1 , wherein the instructions, when executed, cause the system to further perform:

deleting the snippet from the data store upon a time period of inactivity.

9. The system of claim 1 , wherein the obtaining of the data file is from a fast-access storage, and the storing of the snippet is in a slow-access storage.

10. The system of claim 1 , wherein the clustering is based on geolocations associated with the snippet and the one or more other snippets.

11. The system of claim 1 , wherein the instructions, when executed, cause the system to further perform:

storing the one or more other snippets to the data store in accordance with data types of the one or more other snippets.

12. The system of claim 1 , wherein the one or more other snippets are associated with portions of one or more data file tagged by a second user during playback of the one or more data files.

13. A computer-implemented method, the method comprising:

obtaining a data file from a data source through a plug-in interface;

playing the data file for a user through an application running on a computing device;

identifying one or more entities depicted in the data file;

obtaining information relating to the identified entities from a source wherein the obtained information is at least geospatial information associated with the entities, and wherein the source is data objects in an enterprise data platform;

overlaying, through the application, the obtained information during playback of the data file;

receiving an indication from the user, through the application, to tag an entity depicted in the data file during playback of the data file;

clustering the snippet with one or more other snippets depicting the entity prior to storing the snippet; and

storing, to a data store in accordance with a data type of the snippet, the snippet and the one or more other snippets according to the clustering, wherein the snippet is a portion of the data file corresponding to the entity tagged by the user.

14. The computer-implemented method of claim 13 , further comprising:

providing a histogram, through the application, synchronously, during playback of the data file.

15. The computer-implemented method of claim 14 , wherein the histogram comprises one or more bars, each bar representing a number of entities depicted in the data file in a given time period.

16. The computer-implemented method of claim 13 , further comprising:

deleting the snippet from the data store upon a time period of inactivity.

17. A non-transitory computer readable medium storing instructions that, when executed, cause one or more processors to perform:

obtaining a data file from a data source through a plug-in interface;

playing the data file for a user through an application running on a computing device;

identifying one or more entities depicted in the data file;

obtaining information relating to the identified entities from a source wherein the obtained information is at least geospatial information associated with the entities, and wherein the source is data objects in an enterprise data platform;

overlaying, through the application, the obtained information during playback of the data file;

receiving an indication from the user, through the application, to tag an entity depicted in the data file during playback of the data file;

clustering the snippet with one or more other snippets depicting the entity prior to storing the snippet; and

storing, to a data store in accordance with a data type of the snippet, the snippet and the one or more other snippets according to the clustering, wherein the snippet is a portion of the data file corresponding to the entity tagged by the user.

18. The non-transitory computer readable medium of claim 17 , wherein the instructions, when executed, cause the processors to further perform:

providing a histogram, through the application, synchronously, during playback of the data file.

19. The non-transitory computer readable medium of claim 18 , wherein the histogram comprises one or more bars, each bar representing a number of entities depicted in the data file in a given time period.