Local Search Request Processing with Probabilistic Result Refresh
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Solution Overview
Problem
Existing database-based query systems face challenges in maintaining up-to-date search results at user terminals, leading to desynchronized data and increased computational and transmission loads on remote databases, which may become unsustainable with future energy scarcity.
Innovation Solution
User terminals employ a probabilistic model and re-computation rules to locally determine the validity of search results and update them independently, reducing the need for frequent requests to remote databases using federated machine learning and deterministic rules.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Power
If search results are stored at user terminals to reduce server workload, then server computational load is reduced, but data desynchronization and outdated results occur
Solution Approach 1:
The system pre-calculates validity probabilities and stores them alongside search results. When a trigger event occurs, the terminal uses these pre-stored probabilities to quickly determine if re-computation is needed, rather than querying the server first. This preliminary preparation enables efficient local decision-making that maintains data freshness while minimizing server interactions.
Solution Approach 2:
The user terminal autonomously determines the validity of stored search results using locally stored probabilistic models and trigger events. The terminal self-manages the re-computation process by comparing validity probabilities against thresholds and automatically re-computing results when needed, without requiring server intervention for each validity check. This self-service mechanism ensures data freshness while reducing server load.
2Reliability
If search results are frequently re-computed to maintain data freshness, then data accuracy is improved, but energy consumption and computational load increase
Solution Approach 1:
Instead of re-computing all search results at fixed intervals or upon every user interaction, the system applies partial re-computation only to specific results whose validity probabilities fall below predetermined thresholds. This selective approach ensures data accuracy for critical results while avoiding unnecessary energy consumption for results that remain valid, achieving energy-efficient maintenance of data freshness.
Solution Approach 2:
The system dynamically changes the re-computation decision based on the validity probability parameter. By comparing the probability that a search result is outdated against a threshold, the system adaptively determines whether re-computation is necessary. This parameter-based decision mechanism optimizes the balance between data accuracy and energy consumption by re-computing only when statistically necessary.
3Productivity
If probabilistic models are used to determine search result validity, then re-computation frequency is optimized, but device complexity increases
Solution Approach 1:
The system copies the probabilistic model and validity determination logic from the server environment to the user terminal. By storing the model locally and executing it on the terminal device, the system enables efficient local re-computation decisions without requiring complex server-terminal coordination. This copying approach simplifies the overall system architecture while maintaining re-computation efficiency.
Data Source
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AI summary
The present disclosure relates to the processing of search results in a distributed computing system, especially at a user terminal. A user terminal comprises a memory storing a probabilistic model to determine validities of search results being outdated after passed time and storing rules to re-compute the search results. The user terminal receives, in response to at least one search request to at least one search platform, at least one search result from the search platform. The user terminal stores the received search results, and in response to a trigger event, locally determines, based on a probabilistic model, that the at least search result is likely invalid. The user terminal locally re-computes the at least one search result using the rules and presents the at least one re-computed search result to the user.