Dynamic Data Model Supplementation via External Sources
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Computing systems face challenges in responding to requests for information when the data model lacks sufficient information, as they are unable to interpret or provide data for queries due to insufficient data sources within the model.
Innovation Solution
The system identifies and automatically supplements the data model with external data sources that contain relevant information, allowing it to respond to requests by integrating additional data sets and relationships, which can be done proactively based on anticipated future requests or in real-time as queries are processed.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If the data model is kept simple and static, then the system structure is easy to maintain and understand, but the system cannot respond to requests when information is insufficient within the data model
Solution Approach 1:
The data model transitions from a static structure to a dynamic one that can automatically supplement itself with external data sources when information is insufficient. The system dynamically identifies and integrates additional data sources based on query requirements, allowing the data model to adapt its structure in response to information needs while maintaining operational reliability.
Solution Approach 2:
The data model implements self-service by automatically identifying when information is insufficient and autonomously supplementing itself with external data sources. The system performs self-augmentation without manual intervention, continuously improving its own capability to respond to queries by integrating relevant external data while managing its structural complexity.
2Adaptability or versatility
If external data sources are automatically integrated, then the data model can respond to more queries, but the system complexity increases due to data integration and management
Solution Approach 1:
The system introduces an intermediary mechanism that automatically manages the integration between the core data model and external data sources. This intermediary layer handles data identification, integration, and coordination, allowing the system to expand its query coverage through multiple data sources while abstracting away the complexity of data management from the core system.
Solution Approach 2:
The data model system achieves multi-functionality by being able to respond to diverse query types across different domains through automatic supplementation with various external data sources. The system maintains a universal capability to handle different information needs while managing the complexity of integrating multiple specialized data sources through a unified framework.
3Measurement precision
If the data model is supplemented in real-time with external data sources, then query response accuracy improves, but the processing time increases
Solution Approach 1:
The system performs preliminary actions by proactively identifying and integrating external data sources before queries are submitted. By pre-supplementing the data model with relevant external data based on anticipated query patterns or data usage analysis, the system reduces the processing time required during actual query operations while maintaining high information accuracy through pre-integrated data sources.
Data Source
AI summary
Mechanisms to help a computing system respond to a request for information within a data model. After determining that there is insufficient information within the data model to respond to the request, the computing system identifies one or more additional data sources that are external to the data model and that contain information suitable to respond to the request. The computing system then automatically supplements the data model with at least one of such additional data sources. The computing system then responds to the request using the supplemented data model. In some embodiments, the supplementation is performed in advance of the request by analyzing the characteristics of the data model and/or by anticipating possible future requests. Thus, a data model grows automatically in response to particular usage of that data model to satisfy requests.