Query Engine Auxiliary 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 and management of large datasets across organizations.
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 data operations through an interactive workspace interface.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If conventional data management applications are used across disparate computing platforms, then data storage and processing can be performed, but data silos are created that limit interoperability and effective data use across organizations
Solution Approach 1:
The patent implements an intermediary layer (data federation engine or virtualization layer) that sits between disparate data sources and users, enabling seamless data access and interoperability without requiring manual integration. This intermediary automatically handles data translation, format conversion, and protocol adaptation, resolving the contradiction by providing high adaptability while keeping integration complexity hidden from end users.
Solution Approach 2:
The system employs universal data access interfaces and standardized protocols that allow a single platform to interact with multiple different data sources and applications. This multi-functional approach enables the system to work across diverse computing platforms and database technologies, achieving broad data interoperability without proportionally increasing integration complexity.
2Measurement precision
If data scientists create complex data models using sophisticated analysis tools, then accurate predictive models can be developed, but manual intervention is required and other users lack the skill to use these tools
Solution Approach 1:
The system implements self-service capabilities that allow users to automatically access and apply predictive data models without requiring manual intervention from data scientists. The platform enables non-expert users to leverage sophisticated analytics through automated model deployment, standardized interfaces, and self-explanatory tools, thereby maintaining high predictive accuracy while dramatically improving ease of operation.
Solution Approach 2:
The patent segments the complex data modeling process into distinct, manageable components that can be independently deployed and accessed. By breaking down sophisticated analysis tools into modular, reusable data models with standardized interfaces, the system allows accurate predictive modeling to be performed by experts while enabling broader user access through simplified, pre-packaged analytical solutions.
3Adaptability or versatility
If disparate database technologies and data formats are used across computing platforms, then data can be stored and managed independently, but data silos are created that segregate and isolate datasets
Solution Approach 1:
The system employs an intermediary data federation layer that transparently connects disparate database technologies and data formats, enabling unified data access without loss of information. This intermediary automatically handles data translation, format conversion, and protocol adaptation, ensuring that data remains accessible and interoperable across different platforms while maintaining the independence of individual data sources.
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.