Auxiliary Query Commands for Predictive Model Deployment
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Solution Overview
Problem
Conventional data management and analysis applications face challenges in interoperability and efficient data operations due to 'data silos' created by disparate computing platforms and database technologies, which limits the effective use of large datasets across organizations with varying skill levels.
Innovation Solution
A collaborative dataset consolidation system that implements auxiliary query commands to deploy predictive data models, enabling seamless data interoperability and management by using a query engine configured to process auxiliary query commands, which can supplement standard query languages like SQL and SPARQL, and facilitate the integration of predictive data models into datasets.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If conventional data management applications are used, then data operations can be performed within individual systems, but data silos are created that prevent interoperability across disparate computing platforms and database technologies
Solution Approach 1:
The patent implements a universal query processing framework that can execute predictive data model commands across multiple disparate database technologies and computing platforms. The system provides a unified interface that translates standard query commands into platform-specific operations, enabling data operations to be performed universally across different data silos without requiring separate systems for each platform.
Solution Approach 2:
The patent introduces an intermediary query processing layer that sits between the user and disparate database systems. This intermediary framework translates and routes queries to appropriate predictive data models and database technologies, facilitating interoperability between data silos while abstracting away the underlying complexity of each individual system.
2Productivity
If manual intervention is required to apply derived formulaic data models to datasets, then data practitioners can control the process, but productivity decreases and the process becomes time-consuming
Solution Approach 1:
The patent implements self-service capabilities where the query processing framework automatically discovers, selects, and applies appropriate predictive data models to datasets based on the query commands. The system autonomously handles model deployment, parameter tuning, and result generation without requiring manual intervention from data practitioners, thereby significantly improving productivity while maintaining operational control through the unified interface.
3Productivity
If predictive data models are deployed automatically during query execution, then productivity increases and interoperability improves, but device complexity and computational resources increase
Solution Approach 1:
The patent employs preliminary action by pre-compiling and optimizing predictive data models before query execution. The system prepares model deployment configurations, validates query commands against available models, and pre-loads necessary computational resources in advance. This preliminary preparation reduces the complexity and resource requirements during actual query execution, enabling automatic model deployment to proceed efficiently without overwhelming computational demands.
Data Source
AI summary
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.