Auxiliary Query Commands for Predictive Model Deployment
Find Innovative SolutionsGenerate Solutions
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 ability of organizations to effectively utilize and manage large amounts of enterprise data.
Innovation Solution
A collaborative dataset consolidation system that includes a query engine, data project controller, and collaboration manager, enabling the implementation of auxiliary query commands to deploy predictive data models across disparate datasets, facilitating data interoperability and automated execution of machine learning algorithms without manual intervention.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If conventional data management and analysis applications are used across disparate computing platforms and database technologies, then data operations can be performed within individual systems, but data silos are created that limit interoperability and organization-wide data utilization
Solution Approach 1:
The patent introduces a query engine as an intermediary component that sits between users and disparate data sources. This query engine translates high-level queries into platform-specific operations, enabling seamless data access across different computing platforms and database technologies without requiring direct integration between each system. The query engine acts as a mediator that handles the complexity of interoperability, allowing organizations to utilize data across silos while maintaining system independence.
2Reliability
If data scientists create complex data models using sophisticated analysis application tools, then accurate predictive models can be developed, but manual intervention is required to apply these models to datasets, reducing productivity
Solution Approach 1:
The patent implements self-service capabilities where the query engine automatically discovers, loads, and applies predictive data models to relevant datasets without requiring manual intervention from data scientists. The system autonomously identifies which models are applicable to which data, executes the analyses, and integrates results back into the data environment. This automation maintains model accuracy while dramatically improving deployment efficiency and enabling non-expert users to benefit from sophisticated analytics.
3Adaptability or versatility
If users with varying skill levels access different analytic data tools, then specialized functions can be utilized, but disparities in tool interfaces and skill requirements frustrate efforts to improve interoperability and usage
Solution Approach 1:
The patent creates a universal query interface that provides multi-functional access to diverse data sources and analytical capabilities through a single standardized interface. The query engine translates various query types and handles multiple data formats, allowing users with different skill levels to access the same data and analytical functions without needing to learn multiple specialized tools. This universal interface maintains the sophisticated analytical capabilities while significantly improving ease of operation and interoperability across the organization.
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.