Auxiliary query commands to deploy predictive data models for queries in a networked computing platform
Various embodiments relate generally to data science and data analysis, computer software and systems, and network communications to interface among repositories of disparate datasets and computing machine-based entities configured to access datasets, and, more specifically, to a computing and data storage platform configured to provide one or more computerized tools to deploy predictive data models based on in-situ auxiliary query commands implemented in a query, and configured to facilitate development and management of data projects by providing an interactive, project-centric workspace interface coupled to collaborative computing devices and user accounts. For example, a method may include activating a query engine, implementing a subset of auxiliary instructions, at least one auxiliary instruction being configured to access model data, receiving a query that causes the query engine to access the model data, receiving serialized model data, performing a function associated with the serialized model data, and generating resultant data.
1. A method comprising:
detecting a query request;
identifying data representing serialized model data that includes a format associated with a model data;
causing presentation of user inputs to a user interface configured to perform a query responsive in association with the query request;
receiving query data referencing parameters, a dataset, and a predictive data model;
executing a query based on the query associated with data configured to access at least a portion of a graph data arrangement being formatted in a triple-based data format; and
identifying resultant data including data representing a degree of confidence relative to the predictive data model used to determine the resultant data.
2. The method of claim 1 , further comprising:
accessing one or more memory repositories to load the dataset data and the predictive data model into computational memory to execute a function associated with the predictive data model.
3. The method of claim 2 , wherein accessing the one or more memory repositories comprises:
accessing one or more triplestore databases.
4. The method of claim 2 , further comprising:
extracting subsets of the dataset data in accordance to the parameters; and
applying the subset of the dataset data to inputs of the predictive data model to execute the function.
5. The method of claim 4 , further comprising:
generating the resultant data at outputs of the predictive data model; and
generating the data representing the degree of confidence for each result.
6. The method of claim 1 , further comprising:
formatting subsets of the resultant data and corresponding data representing degrees of confidence in tabular data format; and
causing presentation of the tabular data format at the user interface.
7. The method of claim 1 , wherein causing the presentation of the user inputs comprises:
implementing an auxiliary query command.
8. The method of claim 1 , wherein executing the query comprises:
causing presentation of a field in which to receive a query command.
9. The method of claim 8 , wherein the query command is a structured query language (“SQL”) statement.
10. The method of claim 8 , wherein the query command is a SPARQL protocol and RDF query language (“SPARQL”) statement.
11. An apparatus comprising:
a memory including executable instructions; and
a processor, responsive to executing the instructions, is configured to:
detect a query request;
receive data representing serialized model data that includes a format associated with a model data;
cause presentation of user inputs to a user interface configured to perform a query in association with the query request;
receive query data referencing parameters, a dataset, and a predictive data model;
execute a query based on the query data associated with data configured to access at least a portion of a graph data arrangement being formatted in a triple-based data format; and
identify resultant data including data representing a degree of confidence relative to the predictive data model used to determine the resultant data.
12. The apparatus of claim 11 wherein a subset of the instructions further causes the processor to:
access one or more memory repositories to load the dataset data and the predictive data model into computational memory to execute a function associated with the predictive data model.
13. The apparatus of claim 12 wherein a subset of the instructions further causes the processor to:
access one or more triplestore databases.
14. The apparatus of claim 12 wherein a subset of the instructions further causes the processor to:
extract subsets of the dataset data in accordance to the parameters; and
apply the subset of the dataset data to inputs of the predictive data model to execute the function.
15. The apparatus of claim 14 wherein a subset of the instructions further causes the processor to:
generate the resultant data at outputs of the predictive data model; and
generate the data representing the degree of confidence for each result.
16. The apparatus of claim 11 wherein a subset of the instructions further causes the processor to:
format subsets of the resultant data and corresponding data representing degrees of confidence in tabular data format; and
cause presentation of the tabular data format at the user interface.
17. The apparatus of claim 11 wherein a subset of the instructions further causes the processor to:
implement an auxiliary query command.
18. The apparatus of claim 17 wherein a subset of the instructions further causes the processor to:
cause presentation of a field in which to receive a query command.
19. The apparatus of claim 18 wherein the query command is a structured query language (“SQL”) statement.
20. The apparatus of claim 18 , wherein the query command is a SPARQL protocol and RDF query language (“SPARQL”) statement.