Aggregator Predictive Routing for Search Requests
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Computing systems face inefficiencies in handling large volumes of computationally complex search requests, leading to excessive resource expenditure and wasted computational cycles due to the generation and transmission of numerous search results, many of which are discarded without providing commercial return.
Innovation Solution
An aggregator system that stores historical data on previous search requests and their outcomes, allowing it to predict the likelihood of supplier subsystems generating relevant results, thereby selectively routing search requests to only those likely to produce relevant results, reducing unnecessary resource allocation and optimizing computational impact.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If the system generates and transmits numerous search results to client subsystems, then the completeness of search results is improved, but the computational resources expended and wasted cycles increase significantly
Solution Approach 1:
The aggregator performs preliminary evaluation of supplier subsystems by analyzing historical data before receiving new search requests. It predicts the likelihood of each supplier generating relevant results in advance, enabling selective routing of search requests only to suppliers with high predicted relevance, thereby avoiding unnecessary computational resources being spent on suppliers likely to return irrelevant results
Solution Approach 2:
The system uses historical outcome indicators from previous search requests as feedback to evaluate and predict supplier performance. By analyzing whether suppliers previously generated results meeting relevance thresholds, the system continuously refines its predictions and adjusts routing decisions, creating a feedback loop that optimizes resource allocation over time
2Adaptability or versatility
If the system routes search requests to multiple supplier subsystems, then the coverage of search results is improved, but the complexity of request handling increases
Solution Approach 1:
The system extracts and analyzes only the critical historical outcome indicators from vast amounts of past search request data. By focusing on whether suppliers met relevance thresholds in previous requests, the system simplifies the evaluation process and reduces the complexity of analyzing supplier performance while still maintaining comprehensive coverage through predictive routing
3Productivity
If the system processes large volumes of search requests, then the responsiveness to client needs is improved, but the computational complexity and resource consumption increase significantly
Solution Approach 1:
The aggregator performs preliminary evaluation of supplier subsystems by analyzing historical data before receiving new search requests. It predicts the likelihood of each supplier generating relevant results in advance, enabling selective routing of search requests only to suppliers with high predicted relevance, thereby avoiding unnecessary computational resources being spent on suppliers likely to return irrelevant results
Solution Approach 2:
The system changes the parameter of evaluation from processing all search requests uniformly to selectively processing requests based on predicted supplier performance. By transforming the approach from volume-based processing to probability-based routing, the system maintains high productivity while reducing computational complexity through intelligent parameter selection
Data Source
Figure 1
Figure 2
Figure 3
AI summary
A method includes: at an aggregator, storing historical data representing: (i) a plurality of previous search requests received at the aggregator, and (ii) for each previous search request, an outcome indicator defining whether a supplier subsystem generated previous search results meeting a relevance threshold in response to the previous search request; receiving, at the aggregator from a client subsystem, a search request containing a set of client search parameters; in response to receiving the search request, determining, based on the search request and the historical data, a likelihood of the supplier subsystem generating search results meeting the relevance threshold; and selecting, according to the likelihood, a routing action for the search request, relative to the supplier subsystem.