Methods and systems for undetermined query analytics
Set analysis may be used to determine the best data analysis model(s) (e.g., data charts, data tables, data graphs, data maps, graphical objects, key performance indicators (KPIs), etc.) for representing the results of an undetermined query (e.g., an imprecise query, an undefined query, an incomplete query, a partially expressed query, a portioned query, etc.).
1. A method comprising:
determining, based on undetermined query information, one or more data constraints and a plurality of data analysis models;
determining, based on the one or more data constraints, a first aggregated dataset and a second aggregated dataset, wherein the first aggregated dataset comprises in-memory data and the second aggregated dataset comprises externally-sourced data, and wherein the in-memory data at least partially differs from the externally-sourced data; and
causing, based on the first aggregated dataset and the second aggregated dataset, a first data analysis model of the plurality of data analysis models to be output, wherein one or more portions of the second aggregated dataset correspond to one or more visual elements of the first data analysis model, wherein causing the first data analysis model to be output comprises: determining an amount of correspondence between elements of each data analysis model of the plurality of data analysis models and the one or more portions of the second aggregated dataset.
2. The method of claim 1 , wherein at least one of:
the in-memory data is associated with at least one user interface selection;
the externally-sourced data is associated with an external engine; or
the externally-sourced data is associated with a public domain.
3. The non-transitory computer-readable medium of claim 1 , wherein at least one of:
the in-memory data is associated with at least one user interface selection;
the externally-sourced data is associated with an external engine; or
the externally-sourced data is associated with a public domain.
4. The method of claim 1 , wherein the undetermined query information comprises one or more of imprecise query information, undefined query information, incomplete query information, partially expressed query information, or portioned query information.
5. The method of claim 1 , wherein the one or more data constraints comprise one or more temporal data constraints, logical data constraints, or data-type constraints.
6. The method of claim 1 , wherein determining the one or more data constraints comprises mapping one or more textual elements of the undetermined query information to the one or more data constraints.
7. The method of claim 1 , wherein the plurality of data analysis models comprises one or more of: a data chart, a data table, a data graph, a data map, or key performance indicators (KPIs).
8. The method of claim 1 , wherein determining the first aggregated dataset and the second aggregated dataset comprises applying the one or more data constraints to an aggregation function.
9. The method of claim 1 , wherein the second aggregated dataset comprises one or more portions of data that correspond to one or more elements of the first data analysis model.
10. A non-transitory computer-readable medium storing processor-executable instructions that, when executed by at least one processor, cause the at least one processor to:
determine, based on undetermined query information, one or more data constraints and a plurality of data analysis models;
determine, based on the one or more data constraints, a first aggregated dataset and a second aggregated dataset, wherein the first aggregated dataset comprises in-memory data and the second aggregated dataset comprises externally-sourced data, and wherein the in-memory data at least partially differs from the externally-sourced data; and
cause, based on the first aggregated dataset and the second aggregated dataset, a first data analysis model of the plurality of data analysis models to be output, wherein one or more portions of the second aggregated dataset correspond to one or more visual elements of the first data analysis model, wherein the processor-executable instructions that cause the at least one processor to cause the first data analysis model to be output further cause the at least one processor to determine an amount of correspondence between elements of each data analysis model of the plurality of data analysis models and the one or more portions of the second aggregated dataset.
11. The non-transitory computer-readable medium of claim 10 , wherein the undetermined query information comprises one or more of imprecise query information, undefined query information, incomplete query information, partially expressed query information, or portioned query information.
12. The non-transitory computer-readable medium of claim 10 , wherein the one or more data constraints comprise one or more temporal data constraints, logical data constraints, or data-type constraints.
13. The non-transitory computer-readable medium of claim 10 , wherein the processor-executable instructions that cause the at least one processor to determine the one or more data constraints further cause the at least one processor to map one or more textual elements of the undetermined query information to the one or more data constraints.
14. The non-transitory computer-readable medium of claim 10 , wherein the plurality of data analysis models comprises one or more of: a data chart, a data table, a data graph, a data map, or key performance indicators (KPIs).
15. The non-transitory computer-readable medium of claim 10 , wherein the processor-executable instructions that cause the at least one processor to determine the first aggregated dataset and the second aggregated dataset further cause the at least one processor to apply the one or more data constraints to an aggregation function.
16. The non-transitory computer-readable medium of claim 10 , wherein the second aggregated dataset comprises one or more portions of data that correspond to one or more elements of the first data analysis model.
17. An apparatus comprising:
one or more processors; and
memory storing processor-executable instructions that, when executed by the one or more processors, cause the apparatus to:
determine, based on undetermined query information, one or more data constraints and a plurality of data analysis models;
determine, based on the one or more data constraints, a first aggregated dataset and a second aggregated dataset, wherein the first aggregated dataset comprises in-memory data and the second aggregated dataset comprises externally-sourced data, and wherein the in-memory data at least partially differs from the externally-sourced data; and
cause, based on the first aggregated dataset and the second aggregated dataset, a first data analysis model of the plurality of data analysis models to be output, wherein one or more portions of the second aggregated dataset correspond to one or more visual elements of the first data analysis model, wherein the processor-executable instructions that cause the one or more processors to cause the apparatus to cause the first data analysis model to be output further cause the apparatus to determine an amount of correspondence between elements of each data analysis model of the plurality of data analysis models and the one or more portions of the second aggregated dataset.
18. The apparatus of claim 17 , wherein at least one of:
the in-memory data is associated with at least one user interface selection;
the externally-sourced data is associated with an external engine; or
the externally-sourced data is associated with a public domain.
19. The apparatus of claim 17 , wherein the processor-executable instructions that cause the apparatus to determine the one or more data constraints further cause the apparatus to map one or more textual elements of the undetermined query information to the one or more data constraints.
20. The apparatus of claim 17 , wherein the processor-executable instructions that cause the apparatus to determine the first aggregated dataset and the second aggregated dataset further cause the apparatus to apply the one or more data constraints to an aggregation function.