Pre-computed Search Result Validity via Confidence Factors
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Solution Overview
Problem
Pre-computed search results in database systems often become outdated, leading to incorrect results being returned to clients due to changes in underlying primary data, and existing strategies for re-computation or re-collection do not achieve 100% validity or correctness.
Innovation Solution
A mechanism that estimates the validity of pre-computed search results using confidence factors associated with each result, filtering out results with confidence values below a specified threshold to ensure only likely valid results are returned to the client, thereby increasing the accuracy of search results while maintaining short response times.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of time
If pre-computed search results are maintained in a pool to speed up query response, then response time is reduced, but the results become outdated and lose validity when underlying primary data changes
Solution Approach 1:
The system pre-computes search results and stores them in a pool before queries are submitted. This preliminary action allows the system to answer queries by simply retrieving pre-computed results rather than processing data in real-time, thus achieving fast response times while maintaining a mechanism to track and update validity when underlying data changes
Solution Approach 2:
The system implements a feedback mechanism where the validity or freshness of pre-computed search results is continuously monitored. When underlying primary data changes are detected, the system receives feedback and updates or re-computes the affected search results in the pool, ensuring reliability is maintained while preserving the performance benefits of pre-computation
2Reliability
If re-computation strategy is optimized to re-collect results with priority when likely outdated, then validity is improved, but processing complexity and resources increase
Solution Approach 1:
The system changes parameters such as priority levels, time thresholds, and validity metrics to dynamically determine which pre-computed results need re-computation. By adjusting these parameters based on data change patterns and query frequencies, the system optimizes the re-computation strategy to improve validity while managing processing complexity and resource utilization efficiently
3Measurement precision
If confidence factors are used to filter pre-computed search results, then result accuracy is improved, but the number of valid results returned may decrease
Solution Approach 1:
The system applies confidence factors locally to individual search results rather than uniformly filtering all results. Each result is evaluated with its specific confidence factor, and only those below a threshold are filtered out. This local quality approach ensures high accuracy for returned results while minimizing the loss of potentially useful information, balancing precision with quantity
Data Source
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AI summary
A method, a search platform, a system and a storage medium for handling queries in a database system are disclosed. The database system includes at least one client and at least one search platform. The search platform maintains pre-collected search results which are associated which confidence factors. A confidence factor indicates a probability of the associated pre-collected search result being valid. The search platform receives a query indicating at least one search criterion to the search platform. The confidence factors associated with the identified pre-collected search results are utilized to increase the mean probability of pre-collected search results returned to the client of being valid. For example, pre-collected search results complying with the at least one search criterion and being associated with confidence factors having values exceeding a given threshold are returned to the client.