Method and system for bundling automotive data

1. A method of bundling data features relating to connected vehicles, in accordance with respective use cases, the method comprising:

obtaining a plurality of data features relating to connected vehicles originated from a plurality of data sources;

maintaining a use cases database, holding a plurality of use cases, each affiliated with a respective blend of said data features;

generating a plurality of data features bundles, wherein each bundle is a list of attributes that form a subset of data features that is sufficient to implement one of said use cases;

receiving one or more requests from clients for data features, each request associated with one or more respective business requirements; and

providing said clients with a respective one of said data features bundles, according to the respective one or more business requirements and responsive to the requests and based on the use cases database.

2. The method according to claim 1 , wherein said data features bundles are pre-generated prior to receiving the requests.

3. The method according to claim 1 , wherein said data features bundles are generated on-the-fly based on predefined rules, responsive to receiving the requests.

4. The method according to claim 1 , wherein said data features bundles are generated based on market research.

5. The method according to claim 1 , wherein said data features bundles are generated using machine learning.

6. The method according to claim 1 , wherein said data features bundles comprise a respective blend of data features, wherein the blend administers at least one of types, formats, accuracy, frequency, latency, and sufficiency of the data features.

7. A system for bundling data features relating to connected vehicles, in accordance with respective use cases, the system comprising:

a server configured to obtain a plurality of data features relating to connected vehicles originated from a plurality of data sources;

a use cases database, configured to hold a plurality of use cases, each affiliated with a respective blend of said data features; and

a bundle manager implemented by a computer processor configured to:

store a plurality of data features bundles, wherein each bundle is a list of attributes that form a subset of data features that is sufficient to implement one of said use cases; and

receive one or more requests from clients for data features, each request associated with respective business requirements; and

provide said clients with a respective one of said bundles according to the respective business requirements and responsive to the requests and based on the use cases database.

8. The system according to claim 7 , farther comprising a bundles generator wherein said bundles generator is configured to generate said data features bundles prior to receiving the requests.

9. The system according to claim 7 , further comprising a bundles generator wherein said bundles generator is configured to generate said data features bundles on based on predefined rules, responsive to receiving the requests.

10. The system according to claim 7 , further comprising a bundles generator wherein said bundles generator is configured to generate said data features bundles based on market research.

11. The method according to claim 7 , further comprising a bundles generator wherein said bundles generator is configured to generate said data features bundles using machine learning.

12. The system according to claim 7 , wherein said data features bundles comprise a respective blend of data features, wherein the blend administers at least one of types, formats, accuracy, frequency, latency, and sufficiency of the data features.

13. A non-transitory computer readable storage medium for bundling data features relating to connected vehicles, in accordance with respective use cases, the computer readable storage medium comprising a set of instructions that, when executed, cause at least one computer processor to:

obtain a plurality of data features relating to connected vehicles originated from a plurality of data sources;

hold a plurality of use cases, each affiliated with a respective blend of said data features;

store a plurality of data features bundles, wherein each bundle is a list of attributes that form a subset of data features that is sufficient to implement one of said use cases; and

receive one or more requests from clients for data features, each request associated with respective business requirements; and

provide said clients with a respective one of said bundles according to the respective business requirements and responsive to the requests and based on the use cases database.

14. The non-transitory computer readable storage medium according to claim 13 , further comprising a set of instructions that, when executed, cause the at least one computer processor to generate said data features bundles prior to receiving the requests.

15. The non-transitory computer readable storage medium according to claim 13 , further comprising a set of instructions that, when executed, cause the at least one computer processor to generate said data features bundles on-the-fly based on predefined rules, responsive to receiving the requests.

16. The non-transitory computer readable storage medium according to claim 13 , further comprising a set of instructions that, when executed, cause the at least one computer processor to generate said data features bundles based on market research.

17. The non-transitory computer readable storage medium according to claim 13 , further comprising a set of instructions that, when executed, cause the at least one computer processor to generate said data features bundles using machine learning.

18. The non-transitory computer readable storage medium according to claim 13 , wherein said data features bundles comprise a respective blend of data features, wherein the blend administers at least one of types, formats, accuracy, frequency, latency, and sufficiency of the data features.