Analysis system

1. A computer-implemented method comprising:

receiving, using a request assignment engine, a request to generate a report based on an identified data set;

determining, by the request assignment engine using a machine learning model, a request type associated with the request to generate the report,

wherein, responsive to the machine learning model determining that the request to generate the report is a bursty type request, one or more processors of a first specialized computational resource associated with burst type requests are assigned, and

wherein, responsive to the machine learning model determining that the request to generate the report is a resource intensive type, one or more processors of a second specialized computational resource associated with resource intensive type requests are assigned;

sending, using a request router, the request to generate the report to the assigned, specialized computational resource based on the determined request type;

determining, using the one or more processors of the assigned, specialized computational resource, a first statistical approach to apply to the identified data set automatically;

applying, using the one or more processors of the assigned, specialized computational resource, the first statistical approach to the identified data;

generating, using the one or more processors of the assigned, specialized computational resource, the report including one or more charts based on at least one statistical approach; and

presenting, using the one or more processors of the assigned, specialized computational resource, the report.

2. The computer-implemented method of claim 1 , wherein the first statistical approach to apply to the identified data set is automatically determined by applying a second machine learning model.

3. The computer-implemented method of claim 1 , wherein the one or more charts include at least one of an X-MR chart, an I-MR char, an Xbar-R chart, an Xbar-S chart, C-chart, a u-chart, an np chart, a p-chart, a s-chart, an r-chart, an mr-chart, and a forest plot.

4. The computer-implemented method of claim 1 further including:

determining whether the first statistical approach results in a suitable chart; and

responsive to the first statistical approach resulting in a suitable chart, including the suitable chart in the report; or

responsive to the first statistical approach resulting in an unsuitable chart, applying a second statistical approach.

5. The computer-implemented method of claim 4 , wherein one or more of the first statistical approach and the second statistical approach is in contravention of statistical process norms.

6. The computer-implemented method of claim 4 , wherein one or more of the first statistical approach and the second statistical approach is in contravention of statistical process norms but is determined to yield a suitable result, wherein suitability is based on one or more of a number and a proportion of outliers identified.

7. The computer-implemented method of claim 1 , wherein determining the first statistical approach to apply to the identified data set automatically is responsive to selection of a first graphical element, the method further including:

presenting a user interface to a user on a display, wherein the user interface includes:

the first graphical element that allows a user to selectively activate and deactivate the automatic determination of the first statistical approach;

a second graphical element that determines, based on user input selecting from a plurality of available chart types, a chart type and at least partially overrides the automatic determination of the first statistical approach;

a third graphical element that determines, based on user input, a change point method and at least partially overrides the automatic determination of the first statistical approach;

a fourth graphical element that determines, based on user input, a confidence method and at least partially overrides the automatic determination of the first statistical approach;

a fifth graphical element that determines, based on user input, whether a forecast is presented and at least partially overrides the automatic determination of the first statistical approach; and

a plurality of graphical elements associated with a plurality of user selectable visualizations for user-selectable inclusion in the report.

8. The computer-implemented method of claim 1 , wherein the first specialized computational resource and the second specialized computational resource are specialized based at least in part on a difference in one or more of scaling rules, processing core size, and memory core size.

9. The computer-implemented method of claim 1 , wherein the machine learning model is trained using historical request data including response time.

10. A system comprising:

a processor; and

a memory, the memory storing instructions that, when executed by the processor, cause the system to:

receive, by a request assignment engine, a request to generate a report based on an identified data set;

determining, by the request assignment engine using a machine learning model, a request type associated with the request to generate the report

wherein, responsive to the machine learning model determining that the request to generate the report is a bursty type request, one or more processors of a first specialized computational resource associated with burst type requests are assigned, and

wherein, responsive to the machine learning model determining that the request to generate the report is a resource intensive type, one or more processors of a second specialized computational resource associated with resource intensive type requests are assigned;

send, using a request router, the request to generate the report to the assigned, specialized computational resource based on the determined request type;

determine, using one or more processors of the assigned, specialized computational resource, a first statistical approach to apply to the identified data set automatically;

apply, using the one or more processors of the assigned, specialized computational resource, the first statistical approach to the identified data;

generate, using the one or more processors of the assigned, specialized computational resource, the report including one or more charts based on at least one statistical approach; and

present, using the one or more processors of the assigned, specialized computational resource, the report.

11. The system of claim 10 , wherein the first statistical approach to apply to the identified data set is automatically determined by applying a second machine learning model.

12. The system of claim 10 , wherein the one or more charts include at least one of an X-MR chart, an I-MR char, an Xbar-R chart, an Xbar-S chart, C-chart, a u-chart, an np chart, a p-chart, a s-chart, an r-chart, an mr-chart, and a forest plot.

13. The system of claim 10 , wherein the memory further stores instructions that, when executed by the processor, cause the system to:

determine whether the first statistical approach results in a suitable chart; and

responsive to the first statistical approach resulting in a suitable chart, include the suitable chart in the report; or

responsive to the first statistical approach resulting in an unsuitable chart, apply a second statistical approach.

14. The system of claim 13 , wherein one or more of the first statistical approach and the second statistical approach is in contravention of statistical process norms.

15. The system of claim 13 , wherein one or more of the first statistical approach and the second statistical approach is in contravention of statistical process norms but is determined to yield a suitable result, wherein suitability is based on one or more of a number and a proportion of outliers identified.

16. The system of claim 10 , wherein determining the first statistical approach to apply to the identified data set automatically is responsive to selection of a first graphical element, wherein the memory further stores instructions that, when executed by the processor, cause the system to:

present a user interface to a user on a display, wherein the user interface includes:

the first graphical element that allows a user to selectively activate and deactivate the automatic determination of the first statistical approach;

a second graphical element that determines, based on user input selecting from a plurality of available chart types, a chart type and at least partially overrides the automatic determination of the first statistical approach;

a third graphical element that determines, based on user input, a change point method and at least partially overrides the automatic determination of the first statistical approach;

a fourth graphical element that determines, based on user input, a confidence method and at least partially overrides the automatic determination of the first statistical approach;

a fifth graphical element that determines, based on user input, whether a forecast is presented and at least partially overrides the automatic determination of the first statistical approach; and

a plurality of graphical elements associated with a plurality of user selectable visualizations for user-selectable inclusion in the report.

17. The system of claim 10 , wherein the first, specialized computational resource and the second, specialized computational resource are specialized based at least in part on a difference in one or more of scaling rules, processing core size, and memory core size.

18. The system of claim 10 , wherein the machine learning model is trained using historical request data including response time.