User-Terminal Probabilistic Search Result Recomputing for Lower Database Load
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Solution Overview
Problem
The challenge of maintaining up-to-date search results at user terminals in a distributed computing system is exacerbated by increased computational and transmission loads, especially with the potential future scarcity of energy and resources, necessitating a more efficient method to keep data synchronized without frequent database requests.
Innovation Solution
User terminals employ a probabilistic model and rules to locally determine the validity of search results and recompute them independently, using federated machine learning to minimize the need for frequent updates from remote databases.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If search results are frequently updated by requesting new data from remote databases, then data freshness and reliability are improved, but computational load and transmission load on remote databases increase
Solution Approach 1:
The system pre-computes validity probabilities and stores them locally at user terminals. When a trigger event occurs, the terminal uses these pre-stored probabilities to determine whether to recompute search results, avoiding the need to frequently query remote databases and reducing their computational load.
Solution Approach 2:
User terminals autonomously determine the validity of stored search results using locally stored probabilistic models and trigger events. The terminals self-manage the decision-making process to recompute results without requiring continuous validation from remote databases, thereby reducing transmission load and improving data freshness locally.
2Speed
If search results are stored locally at user terminals, then access speed and offline capability are improved, but data becomes outdated over time
Solution Approach 1:
The system implements a feedback mechanism where trigger events (such as time-based events or user actions) prompt the terminal to reassess the validity of stored search results using pre-stored probabilistic models. This feedback loop ensures that data is revalidated only when necessary, maintaining both fast local access and updated information.
Solution Approach 2:
Instead of continuous validation, the system uses periodic revalidation triggered by specific events (e.g., time intervals or user interactions). This periodic action maintains data validity while avoiding unnecessary computational overhead and preserving fast local access to search results.
3Productivity
If probabilistic models and rules are stored at user terminals for local validation, then remote database load is reduced, but device complexity at terminals increases
Solution Approach 1:
The system extracts only the essential probabilistic models and validation rules from the remote database and stores them locally at user terminals. This extraction allows terminals to independently validate search results without requiring complex real-time communication with the database, reducing remote load while keeping terminal complexity manageable.
4Reliability
If frequent requests are sent to remote databases to update search results, then data synchronization is improved, but energy consumption and environmental impact increase
Solution Approach 1:
The system pre-computes and stores validity probabilities locally before they are needed. When trigger events occur, terminals use these pre-stored values to make validation decisions, eliminating the need for frequent energy-consuming network requests to remote databases while maintaining data synchronization.
Solution Approach 2:
User terminals autonomously manage their own data validation using locally stored probabilistic models and trigger events. This self-service approach eliminates repeated communication with remote databases, significantly reducing energy consumption and environmental impact while ensuring data remains synchronized through event-driven updates.
Data Source
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.