Interactive and dynamic mapping engine (iDME)
Methods, systems, and apparatuses, among other things, may provide for an interactive dynamic mapping engine (iDME) for business intelligence, which may interactively obtain information for user devices from sources and schema unknown to the user devices.
1. A method comprising:
defining a first base model of an adaptive machine learning model trained with initial domain knowledge;
collecting, by the adaptive machine learning model, inputs from a user device, wherein the inputs include a query for data;
building, by the adaptive machine learning model based on a pattern of the inputs, a user query profile, wherein the user query profile comprises learned patterns of queries;
returning, by the adaptive machine learning model, a result of the query; and
integrating, by the adaptive machine learning model, the user query profile with the first base model of the adaptive machine learning model responsive to a positive evaluation of the result of the query.
2. The method of claim 1 further comprising:
staging, by the adaptive machine learning model based on the first base model and the query, a self-learning model of the adaptive machine learning model;
discerning, by the adaptive machine learning model based on the first base model and the self-learning model, a mature usage pattern;
integrating, by the adaptive machine learning model, the mature usage pattern with the first base model to form a matured model; and
activating, by the adaptive machine learning model, the matured model as a second base model for future evolutions of the adaptive machine learning model.
3. The method of claim 2 , wherein the initial domain knowledge comprises stored information about a telecommunications network inventory, and wherein the first base model includes a starting knowledge base for novice users.
4. The method of claim 3 , wherein discerning the mature usage pattern is based on determining a number of user inputs satisfies a pattern threshold.
5. The method of claim 3 , wherein discerning the mature usage pattern is based on determining a number of user inputs fail to satisfy a pattern threshold.
6. The method of claim 3 , wherein discerning the mature usage pattern includes determining a number of matured patterns reaches a matured pattern threshold value.
7. The method of claim 6 , wherein discerning the mature usage pattern includes adapting the mature usage pattern based on a satisfaction comparison.
8. The method of claim 3 , further comprising evaluating, by the adaptive machine learning model, the result of the query, wherein discerning the mature usage pattern is based on the positive evaluation of the result of the query.
9. A system comprising:
a processor; and
a memory including instructions that, when executed by the processor, cause the system to:
define a first base model of a machine learning model trained with initial domain knowledge;
collect, by the machine learning model, inputs from a user device, wherein the inputs include a query for data;
build, by the machine learning model based on a pattern of the inputs, a user query profile, wherein the user query profile comprises learned patterns of queries;
return, by the machine learning model, a result of the query; and
integrate, by the machine learning model, the user query profile with the first base model of the machine learning model responsive to a positive evaluation of the result of the query.
10. The system of claim 9 wherein the instructions are further configured to cause the system to:
stage, by the machine learning model based on the first base model and the query, a self-learning model of the machine learning model;
discern, by the machine learning model based on the first base model and the self-learning model, a mature usage pattern;
integrate, by the machine learning model, the mature usage pattern with the first base model to form a matured model; and
activate, by the machine learning model, the matured model as a second base model for future evolutions of the machine learning model.
11. The system of claim 10 , wherein discerning the mature usage pattern is based on determining a number of user inputs satisfies a pattern threshold.
12. The system of claim 10 , wherein discerning the mature usage pattern is based on determining a number of user inputs fail to satisfy a pattern threshold.
13. The system of claim 10 , wherein discerning the mature usage pattern includes determining a number of matured patterns reaches a matured pattern threshold value.
14. The system of claim 10 , wherein the instructions are further configured to cause the system to evaluate, by the machine learning model the result of the query, wherein discerning the mature usage pattern is based on the positive evaluation of the result of the query.
15. The system of claim 14 , wherein discerning the mature usage pattern includes adapting the mature usage pattern based on a satisfaction comparison.
16. The system of claim 9 , wherein the first base model includes a starting knowledge base for novice users.
17. A computer program product comprising:
a computer-readable storage medium; and
instructions stored on the computer-readable storage medium that, when executed by a processor, causes the processor to:
define a first base model of a machine learning model including initial domain knowledge;
collect, by the machine learning model, inputs from a user device, wherein the inputs include a query for data;
build, by the machine learning model based on a pattern of the inputs, a user query profile, wherein the user query profile comprises learned patterns of queries;
return, by the machine learning model, a result of the query; and
integrate, by the machine learning model, the user query profile with the first base model of the machine learning model responsive to a positive evaluation of the result of the query.
18. The computer program product of claim 17 wherein the instructions are further configured to cause the processor to:
stage, by the machine learning model based on the first base model and the query, a self-learning model of the machine learning model;
discern, by the machine learning model based on the first base model and the self-learning model, a mature usage pattern;
integrate, by the machine learning model, the mature usage pattern with the first base model to form a matured model; and
activate, by the machine learning model, the matured model as a second base model for future evolutions of the machine learning model.
19. The computer program product of claim 18 , wherein discerning the mature usage pattern is based on determining a number of user inputs satisfies a pattern threshold.
20. The computer program product of claim 17 , wherein the first base model includes a starting knowledge base for novice users.