Selective Cache Population by Data Quality
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Solution Overview
Problem
Existing caching methods face challenges in providing rapid result retrieval and optimal system performance due to inefficiencies in prepopulating caches, such as initial user experience delays in lazy caching and high storage and processing costs in upfront caching, as well as storing unnecessary results in upfront caching.
Innovation Solution
A computer-implemented method for selectively populating a cache preemptively based on data quality, where a set of fields corresponding to a request are analyzed, and only those with high data quality scores are cached, allowing for rapid result retrieval and controlled storage and processing costs.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of time
If upfront caching is used to prepopulate the cache with all possible results, then rapid result retrieval is achieved, but storage and processing costs increase significantly
Solution Approach 1:
The patent applies local quality by evaluating and caching only high-quality results based on data quality measurements rather than caching all results uniformly. The system calculates data quality scores for individual fields and field combinations, selectively caching only those exceeding quality thresholds, thus optimizing storage usage while maintaining rapid retrieval for quality results.
Solution Approach 2:
The patent implements partial action by caching only a subset of results (those with high data quality) rather than all possible results. This selective approach caches sufficient results to provide rapid retrieval for quality data while avoiding the excessive storage costs of caching low-quality or unnecessary results.
2Loss of time
If upfront caching is used to prepopulate the cache, then rapid result retrieval is achieved, but processing costs increase due to analyzing all data
Solution Approach 1:
The system evaluates data quality locally for each field and field combination using quality measurements and thresholds, processing only those combinations that meet quality criteria. This avoids the excessive processing cost of analyzing all possible field combinations while ensuring high-quality results are cached for rapid retrieval.
Solution Approach 2:
The patent performs partial processing by analyzing only field combinations that exceed quality thresholds rather than all possible combinations. This reduces processing costs while still providing rapid retrieval for the subset of high-quality results that are most valuable.
3Quantity of substance
If lazy caching is used to populate the cache on demand, then storage and processing costs are minimized, but initial user experience suffers due to delays
Solution Approach 1:
The patent applies preliminary action by proactively calculating data quality measurements and caching high-quality results before user requests arrive. The system evaluates field quality scores and pre-caches qualifying field combinations, so when users make requests, results are immediately available in the cache without delay, while avoiding storage of low-quality results.
4Reliability
If all field combinations are cached in upfront caching, then complete result coverage is achieved, but unnecessary results consume storage space
Solution Approach 1:
The patent ensures reliability for quality results by caching all high-quality field combinations identified through quality measurements, while improving storage efficiency by excluding low-quality results. The selective caching based on quality thresholds maintains completeness for valuable results while eliminating waste from caching poor-quality data.
Solution Approach 2:
The system achieves partial completeness by caching only the necessary subset of high-quality field combinations rather than all possible combinations. This provides sufficient result coverage for quality data while dramatically improving storage efficiency by excluding unnecessary low-quality results.
Data Source
AI summary
Selectively populating a cache preemptively based on data quality is provided. A set of fields corresponding to a request to analyze data for caching is obtained. A data quality measurement is calculated for fields in the set of fields corresponding to the request based on characteristics of the data in the set of fields. An aggregate data quality for a combination of fields in the set of fields is derived based on aggregation of the data quality measurement of each respective field in the combination. A result of data analysis of the combination of fields in the set of fields corresponding to the request is cached in a local cache based on the aggregate data quality for the combination of fields exceeding a minimum aggregate data quality threshold level.