Federated Data Procurement via Probabilistic Matching Heuristics
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Managing and persisting data from a federated set of sources in a concurrent, distributed, scalable manner, especially in the context of IIoT devices, is challenging due to the need for ongoing updates and validation.
Innovation Solution
A computerized method for federated data procurement using probabilistic information matching combined with domain-specific heuristics, which involves identifying data sources, matching and validating data, and optimizing weights for heuristics based on ongoing data sources and usage.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If data is procured from a federated set of distributed data sources, then data coverage and scalability are improved, but data consistency and validation difficulty increase
Solution Approach 1:
The patent segments the data validation process into multiple independent heuristic rules that can be applied separately to different data sources and data types. Each heuristic rule acts as an independent validation module, making the overall system manageable despite the distributed nature of data sources.
Solution Approach 2:
The system dynamically adjusts parameters of heuristic rules based on data quality observations. By changing parameters such as matching thresholds and validation criteria based on observed data patterns, the system adapts to different data sources while maintaining consistent validation standards.
2Reliability
If ongoing data updates are implemented to keep the database current, then data freshness is improved, but computational overhead and processing time increase
Solution Approach 1:
The system implements periodic data procurement and validation cycles rather than continuous processing. Data is updated at scheduled intervals, allowing the system to batch process updates efficiently while maintaining data freshness without constant computational overhead.
Solution Approach 2:
The system uses feedback from data quality metrics to intelligently schedule updates. When data quality degrades below thresholds, updates are triggered; when quality is high, updates are deferred. This feedback mechanism optimizes the balance between data freshness and processing time.
3Measurement precision
If probabilistic information matching with domain specific heuristics is used, then data matching accuracy is improved, but system complexity and heuristic optimization difficulty increase
Solution Approach 1:
The patent applies different heuristic rules and matching strategies tailored to specific data domains and types. Each data domain has its own optimized set of heuristics, allowing high accuracy for each specific case while managing overall complexity through localized rule sets rather than a single monolithic system.
4Measurement precision
If weights for heuristics are optimized on an ongoing basis, then matching precision is improved, but computational resources and processing overhead increase
Solution Approach 1:
Heuristic weights are optimized periodically rather than continuously. The system schedules weight optimization at intervals or based on trigger conditions such as significant changes in data patterns, reducing computational resource consumption while maintaining matching precision through regular re-optimization.
Data Source
AI summary
In one aspect, a computerized method for federated data procurement using probabilistic information matching via domain specific heuristics. The method includes implementing procurement of the data from a plurality of online data sources. Each online data source comprises a plurality of measures. The method includes matching and validating the data. The method includes associating a plurality of weights with the plurality of set of domain specific heuristics that are optimized on an ongoing basis as newer data sources are identified. The method includes detecting that new information is collected and adding a plurality of additional heuristics to the domain specific heuristic frameworks.