QUERY ENGINE IMPLEMENTING AUXILIARY COMMANDS VIA COMPUTERIZED TOOLS TO DEPLOY PREDICTIVE DATA MODELS IN-SITU 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:
activating a query engine configured to receive and identify data as model data;
implementing a subset of auxiliary instructions configured to supplement a set of instructions, at least one auxiliary instruction being configured to access the model data;
receiving data representing a request to perform a query that causes the query engine to access the model data;
receiving data representing serialized model data that includes a format associated with the model data;
performing a function associated with the serialized model data; and
generating resultant data of the query based the function.
2 . The method of claim 1 , wherein receiving the data representing the request to perform the query comprises:
accessing one or more datasets with which to perform the function.
3 . The method of claim 2 , wherein accessing the one or more datasets comprises:
accessing one or more triple stores.
4 . The method of claim 1 , further comprising:
loading the serialized model data into the query engine responsive to an identifier determined by execution of the at least one auxiliary instruction.
5 . The method of claim 4 , further comprising:
performing a query to generate the resultant data based the identifier that references the serialized model data.
6 . The method of claim 1 wherein generating the resultant data of the query based the function comprises:
receiving a query instruction including one or more parameters and an identifier that references the serialized model data; and
accessing one or more datasets with which to input into the function associated with the identifier.
7 . The method of claim 6 further comprising:
retrieving the serialized model data responsive to the query instruction; and
executing instructions to generate the resultant data.
8 . The method of claim 7 wherein executing instructions to generate the resultant data:
applying a subset of the one or more datasets to inputs of the serialized model data; and
identifying the resultant data at one or more outputs of the serialized model data.
9 . The method of claim 1 further comprising:
performing a function call responsive to the query to fetch the data representing the serialized model data.
10 . The method of claim 1 further comprising:
generating data representing a degree of confidence associated with the resultant data.
11 . An apparatus comprising:
a memory including executable instructions; and
a processor, responsive to executing the instructions, is configured to:
activate a query engine configured to receive and identify data as model data;
implement a subset of auxiliary instructions configured to supplement a set of instructions, at least one auxiliary instruction being configured to access the model data;
receive data representing a request to perform a query that causes the query engine to access the model data;
receive data representing serialized model data that includes a format associated with the model data;
perform a function associated with the serialized model data; and
generate resultant data of the query based the function.
12 . The apparatus of claim 11 wherein a subset of the instructions further causes the processor to:
access one or more datasets with which to perform the function.
13 . The apparatus of claim 12 wherein a subset of the instructions further causes the processor to:
access one or more triple stores.
14 . The apparatus of claim 11 wherein a subset of the instructions further causes the processor to:
load the serialized model data into the query engine responsive to an identifier determined by execution of the at least one auxiliary instruction.
15 . The apparatus of claim 14 wherein a subset of the instructions further causes the processor to:
perform a query to generate the resultant data based the identifier that references the serialized model data.
16 . The apparatus of claim 11 wherein a subset of the instructions further causes the processor to:
receive a query instruction including one or more parameters and an identifier that references the serialized model data; and
access one or more datasets with which to input into the function associated with the identifier.
17 . The apparatus of claim 16 wherein a subset of the instructions further causes the processor to:
retrieve the serialized model data responsive to the query instruction; and
execute instructions to generate the resultant data.
18 . The apparatus of claim 17 wherein a subset of the instructions further causes the processor to:
apply a subset of the one or more datasets to inputs of the serialized model data; and
identify the resultant data at one or more outputs of the serialized model data.
19 . The apparatus of claim 11 wherein a subset of the instructions further causes the processor to:
perform a function call responsive to the query to fetch the data representing the serialized model data.
20 . The apparatus of claim 11 wherein a subset of the instructions further causes the processor to:
generate data representing a degree of confidence associated with the resultant data.