Edge Data Caching Using Confidence Scores and Trust Metadata
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Solution Overview
Problem
Existing data caching mechanisms do not account for data confidence levels, leading to inefficient use of cache resources as lower confidence data is often cached, wasting system resources.
Innovation Solution
Implementing a DCF-supported caching mechanism that uses caching algorithms considering data confidence scores and trust metadata to ensure higher confidence data is prioritized for caching, thereby optimizing cache usage.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional data caching mechanisms are used without considering data confidence levels, then cache resources are used to store any data, but this leads to wasting system resources on storing low confidence data
Solution Approach 1:
The patent introduces a new parameter (data confidence level) into the traditional caching mechanism. The caching algorithm now evaluates data based on confidence scores in addition to traditional caching criteria, transforming the caching decision from a simple key-value operation to a multi-parameter evaluation process that prioritizes high-confidence data
Solution Approach 2:
The patent applies different caching strategies to different data based on their confidence levels. High-confidence data receives prioritized caching treatment while low-confidence data is cached less aggressively or not at all, creating a quality-based differentiation in resource allocation rather than uniform treatment of all data
2Reliability
If data caching is performed without trust metadata verification, then caching operations are simpler and faster, but the confidence in cached data quality is reduced
Solution Approach 1:
The patent performs trust metadata verification and confidence level assessment before data is cached, rather than during or after caching. This preliminary evaluation ensures that only data meeting the trust criteria is considered for caching, preventing low-confidence data from entering the cache in the first place
Solution Approach 2:
The patent introduces trust metadata and confidence scoring as an intermediary layer between data ingestion and caching. This intermediary mechanism evaluates data quality attributes and uses them to inform caching decisions, acting as a filter that bridges the gap between raw data and cached data
Data Source
AI summary
A computing system may receive a data stream from a data source of an edge environment. The computing system may identify at least one of a first confidence score or first trust metadata associated with the data stream received from data source. The computing system may apply a caching algorithm to the data stream, the caching algorithm implementing one or more caching parameters that specify criteria to be used to determine if the data stream is to be placed in a data cache. The computing system may, based on the results of applying the caching algorithm and the one of the first confidence score or first trust metadata, determine if the data stream is to be placed in the data cache.