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 ability of organizations to productively use large amounts of enterprise data, especially given the varying skill levels of users in using analytic data tools.
Innovation Solution
A collaborative dataset consolidation system that includes a query engine configured to implement auxiliary query commands, enabling the deployment of predictive data models by processing data from multiple datasets and providing a unified interface for data operations, thereby facilitating interoperability and usage of data across different platforms and formats.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If conventional data management and analysis applications are used, then data operations can be performed within individual platforms, but data silos are created that limit interoperability and productive use of enterprise data
Solution Approach 1:
The patent implements a universal data model that can represent multiple data formats and structures (relational, hierarchical, graph, unstructured) through a common framework. This allows the system to interoperate with diverse data sources without requiring separate integration mechanisms for each data type, thereby improving interoperability while managing complexity through standardization.
Solution Approach 2:
The patent introduces an intermediary layer consisting of data connectors and adapters that translate between different data formats and the universal data model. This mediator architecture enables seamless interoperability between disparate systems while isolating the complexity of format conversions from end users, allowing the system to handle multiple data sources uniformly.
2Ease of operation
If sophisticated analysis application tools are used by data scientists, then complex data models can be created, but other individuals with varying skill levels cannot effectively use these tools
Solution Approach 1:
The patent segments the data analysis functionality into distinct layers: data access operations, data manipulation operations, and analysis operations. Each layer can be independently accessed and configured, allowing users with different skill levels to engage with appropriate complexity. Basic users can perform simple queries while advanced users can access sophisticated analysis capabilities without forcing all users to master complex tools.
Solution Approach 2:
The patent implements automated features including automatic model deployment, self-service data preparation, and intelligent query optimization. These self-service capabilities reduce the need for manual intervention in complex machine learning operations, making advanced analytical capabilities accessible to users without requiring them to become experts in model deployment and management.
3Productivity
If predictive data models are deployed manually, then model accuracy can be maintained, but manual intervention is required which reduces productivity
Solution Approach 1:
The patent performs preliminary actions by automatically preparing data, selecting appropriate models, and configuring deployment parameters before model deployment. The system pre-processes data to match model requirements, pre-validates model compatibility with target systems, and pre-configures deployment environments, thereby eliminating the need for manual intervention while maintaining model accuracy through systematic automated procedures.
Solution Approach 2:
The patent implements feedback mechanisms that continuously monitor model performance and automatically trigger retraining or redeployment when performance degradation is detected. The system provides feedback loops that capture production data, evaluate model accuracy, and initiate automated model updates, maintaining model accuracy while significantly reducing manual intervention requirements through closed-loop automation.
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.