Context Graph Data Source Matching for Scalable Application Onboarding
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Solution Overview
Problem
Existing automated methods for selecting and configuring data sources for data-driven applications are not scalable beyond a few dozens and require manual effort, as the computational effort to evaluate every possible combination is beyond feasibility, especially in situations with thousands of data sources.
Innovation Solution
A computer-implemented method and system using a context region explorer to identify a context region of a context graph, request and receive data from a context enrichment platform connected to multiple data sources, and make the data available to the application, optimizing resource usage and adaptiveness.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If automated methods evaluate every possible combination of data sources, then data source selection accuracy is improved, but computational effort becomes infeasible with thousands of data sources
Solution Approach 1:
The patent segments the large set of data sources into smaller groups or clusters based on relevance criteria. Instead of evaluating all possible combinations of thousands of data sources, the system divides them into manageable segments that can be processed efficiently, reducing computational effort while maintaining selection accuracy.
Solution Approach 2:
The system performs preliminary filtering and pre-selection of data sources based on initial criteria before the main evaluation process. This preliminary action reduces the search space from thousands of data sources to a smaller subset that is more likely to contain relevant sources, making the subsequent combination evaluation computationally feasible.
2Use of energy by moving object
If manual effort is used to pre-select potential data sources, then computational effort is reduced, but scalability is limited to a few dozens of data sources
Solution Approach 1:
The system implements automated self-service mechanisms that replace manual pre-selection. The context region explorer automatically identifies and selects relevant data sources based on the application's context and requirements, enabling the system to scale to thousands of data sources without requiring manual intervention while maintaining adaptability.
3Quantity of substance
If the system processes all data sources to ensure completeness, then data coverage is improved, but processing time increases significantly
Solution Approach 1:
The system applies partial action by processing only the most relevant data sources identified through context-based filtering, rather than processing all available data sources. This approach maintains adequate data coverage for the specific application context while significantly reducing processing time by excluding irrelevant data sources.
Solution Approach 2:
The system performs preliminary filtering to identify and prioritize the most relevant data sources before main processing. This preliminary action ensures that the subsequent processing focuses on a smaller, more relevant subset of data sources, maintaining data coverage quality while reducing overall processing time.
Data Source
AI summary
A computer-implemented method auto-connects a data-driven application with one or more suitable data sources. The method includes identifying, by a context region explorer based on a description of the application, a context region of a context graph that represents a context of the application; requesting, by the context region explorer, data related to the identified context region from a context enrichment platform connected to a number of data sources; and receiving, by the context region explorer, the requested data from the context enrichment platform and making the received requested data available to the application.