System and method for management of data from deployments
A global data aggregator for managing deployments includes persistent storage and a global data manager. The persistent storage stores globally aggregated data and an algorithm repository. The global data manager obtains deployment aggregated data from a deployment of the deployments; makes a determination that a new type of data is included in the deployment aggregated data; and in response to the determination: performs an action set, based on the new type of the data, to modify a content of future deployment aggregated data obtained from the deployment.
1. A computing device for managing deployments, comprising:
persistent storage for storing:
globally aggregated data associated with a first data provider, and
an algorithm repository; and
a processor that:
obtains deployment aggregated data from a deployment of the deployments;
makes a determination that the deployment aggregated data comprises a new type of data associated with a second data provider different from the first data provider and that the new type of data in the deployment aggregated data that is associated with the second data provider is incompatible with existing data included in the globally aggregated data that is associated with the first data provider; and
in response to the determination:
performs an action set, based on the new type of data and without user intervention, to modify a content of future deployment data obtained from the deployment to reflect one or more changes associated with the new type of data to remedy the incompatibility between the new type of data of the deployment aggregated data and the existing data included in the globally aggregated data.
2. The computing device of claim 1 , wherein performing the action set to modify the content of the future deployment aggregated data obtained from the deployment comprises:
identifying metadata associated with the new type of data;
making a second determination that the metadata is associated with an algorithmically derived data generator stored in the algorithm repository; and
deploying the algorithmically derived data generator to the deployment based on the second determination.
3. The computing device of claim 2 , wherein deploying the algorithmically derived data generator to the deployment comprises:
instantiating a new instance of the algorithmically derived data generator from the deployment,
wherein the new instance of the algorithmically derived data generator is adapted to generate algorithmically derived data using an instance of the new type of data stored in the deployment.
4. The computing device of claim 1 , wherein performing the action set to modify the content of the future deployment aggregated data obtained from the deployment comprises:
identifying metadata associated with the new type of data;
making a second determination that the metadata is not associated with any algorithmically derived data generator stored in the algorithm repository;
generating a new algorithmically derived data generator based on the new type of data; and
deploying the new algorithmically derived data generated as an experimental generator.
5. The computing device of claim 4 , wherein the algorithmically derived data generated by the experimental generator is flagged by the computing device as untrustworthy.
6. The computing device of claim 1 , wherein the computing device is further programmed to:
identify, a deployment of algorithmically derived data generated from a second deployment that is associated with metadata;
make a second determination that the second deployment is providing additional metadata that is similar to the metadata; and
in response to the second determination:
make a third determination that the second deployment is not generating algorithmically derived data associated with the additional metadata; and
deploy an instance of the algorithmically derived data generator to the second deployment.
7. The computing device of claim 6 , wherein deploying the instance of the algorithmically derived data generator to the second deployment causes the second deployment to generate algorithmically derived data associated with the additional metadata.
8. A method of managing deployments by a computing device for managing deployments, comprising:
obtaining deployment aggregated data from a deployment of the deployments;
making a determination that the deployment aggregated data comprises a new type of data associated with a first data provider different from a second data provider associated with globally aggregated data stored in the computing device and that the new type of data in the deployment aggregated data that is associated with the first data provider is incompatible with existing data included in the globally aggregated data that is associated with the second data provider; and
in response to the determination:
performing an action set, based on the new type of data and without user intervention, to modify a content of future deployment aggregated data obtained from the deployment to reflect one or more changes associated with the new type of data to remedy the incompatibility between the new type of data of the deployment aggregated data and the existing data included in the globally aggregated data.
9. The method of claim 8 , wherein performing the action set to modify the content of the future deployment aggregated data obtained from the deployment comprises:
identifying metadata associated with the new type of data;
making a second determination that the metadata is associated with an algorithmically derived data generator stored in an algorithm repository; and
deploying the algorithmically derived data generator to the deployment based on the second determination.
10. The method of claim 9 , wherein deploying the algorithmically derived data generator to the deployment comprises:
instantiating a new instance of the algorithmically derived data generator from the deployment,
wherein the new instance of the algorithmically derived data generator is adapted to generate algorithmically derived data using an instance of the new type of data stored in the deployment.
11. The method of claim 8 , wherein performing the action set to modify the content of the future deployment aggregated data obtained from the deployment comprises:
identifying metadata associated with the new type of data;
making a second determination that the metadata is not associated with any algorithmically derived data generator stored in an algorithm repository;
generating a new algorithmically derived data generator based on the new type of data; and
deploying the new algorithmically derived data generated as an experimental generator.
12. The method of claim 11 , wherein the algorithmically derived data generated by the experimental generator is flagged as untrustworthy.
13. The method of claim 8 , further comprising:
identifying a deployment of algorithmically derived data generated from a second deployment that is associated with metadata;
making a second determination that the second deployment is providing additional metadata that is similar to the metadata; and
in response to the second determination:
making a third determination that the second deployment is not generating algorithmically derived data associated with the additional metadata; and
deploying an instance of the algorithmically derived data generator to the second deployment.
14. The method of claim 13 , wherein deploying the instance of the algorithmically derived data generator to the second deployment causes the second deployment to generate algorithmically derived data associated with the additional metadata.
15. A non-transitory computer readable medium of a computing device for managing deployments comprising computer readable program code, which when executed by a computer processor of the computing device enables the computer processor to perform a method for managing deployments, the method comprising:
obtaining deployment aggregated data from a deployment of the deployments;
making a determination that the deployment aggregated data comprises a new type of data associated with a first data provider different from a second data provider associated with globally aggregated data stored in the computing device and that the new type of data in the deployment aggregated data that is associated with the first data provider is incompatible with existing data included in the globally aggregated data that is associated with the second data provider; and
in response to the determination:
performing an action set, based on the new type of data and without user intervention, to modify a content of future deployment aggregated data obtained from the deployment to reflect one or more changes associated with the new type of data to remedy the incompatibility between the new type of data of the deployment aggregated data and the existing data included in the globally aggregated data.
16. The non-transitory computer readable medium of claim 15 , wherein performing the action set to modify the content of the future deployment aggregated data obtained from the deployment comprises:
identifying metadata associated with the new type of data;
making a second determination that the metadata is associated with an algorithmically derived data generator stored in an algorithm repository; and
deploying the algorithmically derived data generator to the deployment based on the second determination.
17. The non-transitory computer readable medium of claim 16 , wherein deploying the algorithmically derived data generator to the deployment comprises:
instantiating a new instance of the algorithmically derived data generator in the deployment,
wherein the new instance of the algorithmically derived data generator is adapted to generate algorithmically derived data using an instance of the new type of data stored in the deployment.
18. The non-transitory computer readable medium of claim 15 , wherein performing the action set to modify the content of the future deployment aggregated data obtained from the deployment comprises:
identifying metadata associated with the new type of data;
making a second determination that the metadata is not associated with any algorithmically derived data generator stored in an algorithm repository;
generating a new algorithmically derived data generator based on the new type of data; and
deploying the new algorithmically derived data generated as an experimental generator.
19. The non-transitory computer readable medium of claim 18 , wherein algorithmically derived data generated by the experimental generator is flagged as untrustworthy.
20. The non-transitory computer readable medium of claim 15 , further comprising:
identifying a deployment of algorithmically derived data generated from a second deployment that is associated with metadata;
making a second determination that the second deployment is providing additional metadata that is similar to the metadata; and
in response to the second determination:
making a third determination that the second deployment is not generating algorithmically derived data associated with the additional metadata; and
deploying an instance of the algorithmically derived data generator to the second deployment.