Data driven predictive analysis of complex data sets for determining decision outcomes
Systems and methods are provided for data driven predictive analysis of complex data sets for determining decision outcomes. The systems and methods include obtaining a first set of data associated with individuals and obtaining, a second set of data associated with events, wherein the events are associated with at least one of the individuals. The systems and methods further include determining a subset of the individuals from the first set of data based on the first subset of individuals having common attributes with a target individual and determining a second subset of events from the second set of data based on the second subset of events having common attributes with the target events associated with the target individual. Additionally, the systems and methods include aggregating data associated with the second subset of events based on the target events and providing for display output associated with aggregated data.
1. A non-transitory computer readable storage medium storing instructions that are executable by a computing device that includes one or more processors to cause the computing device to perform a method comprising:
obtaining, from one or more data sources, a first set of data associated with a plurality of events;
generating, based on at least one of the content or type of the first set of data, a second set of data;
acquiring information associated with a target individual and one or more target events of a target event path;
determining a first subset of one or more events of a plurality of event paths from the first set of data wherein the first subset of one or more events have one or more common attributes with the target event path;
determining a second subset of one or more events of a plurality of event paths from the second set of data wherein the second subset of one or more events have one or more common attributes with the target event path; and
generating an analytical model based on the first subset of one or more events and second subset of one or more events for estimating attributes of the target event path.
2. The non-transitory computer readable storage medium of claim 1 wherein the one or more data sources are dynamically updated with new data records and wherein the method is performed in response to the dynamic update.
3. The non-transitory computer readable storage medium of claim 1 , wherein the analytical model is used to estimate the cost associated with the target event path.
4. The non-transitory computer readable storage medium of claim 1 , wherein generating an analytical model includes applying statistical operations to data associated with the first subset of one or more events.
5. The non-transitory computer readable storage medium of claim 4 , wherein the statistical operations applied to data associated with the first subset of one or more events generate a weight value associated with each of the one or more target events.
6. The non-transitory computer readable storage medium of claim 1 , wherein the analytical model is provided for output using a visual representation.
7. The non-transitory computer readable storage medium of claim 1 , wherein the target individual and target event path are provided by a user.
8. An electronic device comprising:
a memory storing instructions; and
one or more processors configured to execute the instructions to cause the electronic device to perform;
obtaining, from one or more data sources, a first set of data associated with a plurality of events;
generating, based on at least one of the content or type of the first set of data, a second set of data;
acquiring information associated with a target individual and one or more target events of a target event path;
determining a first subset of one or more events of a plurality of event paths from the first set of data wherein the first subset of one or more events have one or more common attributes with the target event path;
determining a second subset of one or more events of a plurality of events of a plurality of events paths from the second set of data of one or more events have one one or more common attributes with target event path; and
generating an analytical model based on the first subset of one or more events and the second subset of one or more events for estimating attributes of the target event path.
9. The electronic device of claim 8 , wherein the one or more data sets are dynamically updated with new data records and wherein the one or more processors configured to execute the instructions further cause the electronic device to perform obtaining, from the one or more data sources, a first set of data associated with a plurality of events.
10. The electronic device of claim 8 , wherein the analytical model is used to estimate the cost associated with the target event path.
11. The electronic device of claim 8 , wherein generating an analytical model includes applying statistical operations to data associated with the first subset of one or more events.
12. The electronic device of claim 11 , wherein the statistical operations applied to data associated with the first subset of one or more events generate a weight value associated with each of the one or more target events.
13. The electronic device of claim 8 , wherein the one or more processors configured to execute the instructions further cause the electronic device to perform presenting the analytical model using a visual representation.
14. The electronic device of claim 8 , wherein the one or more processors configured to execute the instructions further cause the electronic device to perform obtaining the target individual and target event path from a user.
15. A method performed by one or more processors and comprising:
obtaining, from one or more data sources, a first set of data associated with a plurality of events;
generating, based on at least one of the content or type of the first set of data, a second set of data;
acquiring information associated with a target individual and one or more target events of a target event path;
determining a first subset of one or more events of a plurality of event paths from the first set of data wherein the first subset of one or more events have one or more common attributes with the target event path;
determining a second subset of one or more events of a plurality of event paths from the second set of data wherein the second subset of one or more events have one or more common attributes with the target event path; and
generating an analytical model based on the first subset of one or more events and the second subset of one or more events for estimating attributes of the target event path.
16. The method of claim 15 wherein the one or more data sources are dynamically updated with new data records and wherein the method is performed in response to the dynamic update.
17. The method of claim 15 , wherein the analytical model is used to estimate the cost associated with the target event path.
18. The method of claim 15 , wherein generating an analytical model includes applying statistical operations to data associated with the first subset of one or more events.
19. The method of claim 18 , wherein the statistical operations applied to data associated with the first subset of one or more events generate a weight value associated with each of the one or more target events.
20. The method of claim 15 , wherein the analytical model is provided for presentation using a visual representation.