METHODS FOR ANALYZING INSURANCE DATA AND DEVICES THEREOF

1 . A method for analyzing data comprising:

obtaining, by an insurance data management computing apparatus, vehicle data from one of the plurality of data sources in a plurality of formats;

aggregating, by the insurance data management computing apparatus, the obtained vehicle data based on one or more geographic location data obtained from one of the plurality of sources;

determining, by the insurance data management computing apparatus, a sampling threshold size for sampling the aggregated vehicle data based on one or more threshold rules;

applying, by the insurance data management computing apparatus, one or more machine learning algorithms to the aggregated vehicle data to generate sampling data when the aggregated vehicle data and the associated demographic vehicle data is greater than the determined sampling threshold size; and

representing, by the insurance data management computing apparatus, the generated sampling data in a graphical representation format via a graphical user interface.

2 . The method as set forth in claim 1 further comprising, categorizing, by the insurance data management computing apparatus, the obtained vehicle data based on one or more data categorizing rules.

3 . The method as set forth in claim 1 further comprising, performing, by the insurance data management computing apparatus, a data cluster operation on the aggregated data prior to applying the one or more machine learning algorithms when the aggregated vehicle data is greater than the determined sampling threshold size.

4 . The method as set forth in claim 1 further comprising, performing, by the insurance data management computing apparatus, data validation to the generated sample data.

5 . The method as set forth in claim 1 wherein the generated sample data is stored in a cache memory.

6 . The method as set forth in claim 5 wherein the sample data is obtained from the cache memory to generated the graphical representation.

7 . A non-transitory computer readable medium having stored thereon instructions for analyzing data, comprising executable code, which when executed by at least one processor, cause the processor to:

obtain vehicle data from one of the plurality of data sources in a plurality of formats;

aggregate the obtained vehicle data based on one or more geographic location data obtained from one of the plurality of sources;

determine a sampling threshold size for sampling the aggregated vehicle data based on one or more threshold rules;

apply one or more machine learning algorithms to the aggregated vehicle data to generate sampling data when the aggregated vehicle data and the associated demographic vehicle data is greater than the determined sampling threshold size; and

represent the generated sampling data in a graphical representation format via a graphical user interface.

8 . The medium as set forth in claim 7 further comprises categorize the obtained vehicle data based on one or more data categorizing rules.

9 . The medium as set forth in claim 7 further comprises, perform a data cluster operation on the aggregated data prior to applying the one or more machine learning algorithms when the aggregated vehicle data is greater than the determined sampling threshold size.

10 . The medium as set forth in claim 7 further comprises, perform data validation to the generated sample data.

11 . The medium as set forth in claim 7 wherein the generated sample data is stored in a cache memory.

12 . The medium as set forth in claim 11 wherein the sample data is obtained from the cache memory to generated the graphical representation.

13 . An insurance data management computing apparatus comprising:

a processor; and

a memory coupled to the processor which is configured to be capable of executing programmed instructions comprising and stored in the memory to:

obtain vehicle data from one of the plurality of data sources in a plurality of formats;

aggregate the obtained vehicle data based on one or more geographic location data obtained from one of the plurality of sources;

determine a sampling threshold size for sampling the aggregated vehicle data based on one or more threshold rules;

apply one or more machine learning algorithms to the aggregated vehicle data to generate sampling data when the aggregated vehicle data and the associated demographic vehicle data is greater than the determined sampling threshold size; and

represent the generated sampling data in a graphical representation format via a graphical user interface.

14 . The apparatus as set forth in claim 13 wherein the processor is further configured to be capable of executing the stored programmed instructions to categorize the obtained vehicle data based on one or more data categorizing rules.

15 . The apparatus as set forth in claim 13 wherein the processor is further configured to be capable of executing the stored programmed instructions to perform a data cluster operation on the aggregated data prior to applying the one or more machine learning algorithms when the aggregated vehicle data is greater than the determined sampling threshold size.

16 . The apparatus as set forth in claim 13 wherein the processor is further configured to be capable of executing the stored programmed instructions to perform data validation to the generated sample data.

17 . The apparatus as set forth in claim 13 wherein the generated sample data is stored in a cache memory.

18 . The apparatus as set forth in claim 17 wherein the sample data is obtained from the cache memory to generated the graphical representation.