Aggregator Predictive Routing for Search Requests
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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 value.
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 valuable results, reducing unnecessary resource allocation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If the system processes all search requests from multiple supplier subsystems, then the completeness of search results is improved, but the computational resource expenditure and time consumption increase significantly
Solution Approach 1:
The system performs preliminary actions by storing historical outcome indicators for each supplier subsystem and search request combination before processing new search requests. This historical data is used to predict which supplier subsystems are likely to generate relevant results, allowing the system to pre-select appropriate suppliers and avoid wasting computational resources on unlikely candidates.
Solution Approach 2:
The system uses its own historical performance data to make routing decisions. By analyzing past outcomes where search results were evaluated against relevance thresholds, the system independently determines which supplier subsystems to query for new search requests, eliminating the need for external guidance or manual configuration.
2Reliability
If the system queries all supplier subsystems for every search request, then the coverage of potential results is improved, but the time to generate responses deteriorates
Solution Approach 1:
The system performs preliminary classification by storing historical outcome indicators that capture whether each supplier subsystem generated relevant results for previous search requests. This pre-processed historical information enables rapid prediction of which suppliers to query, avoiding the time-consuming process of querying all suppliers for every new search request.
3Reliability
If the system generates and transmits numerous search results from all supplier subsystems, then the completeness of result set is improved, but the network resource usage and data processing load increase
Solution Approach 1:
The system extracts only the necessary information by using historical outcome indicators to identify and query only those supplier subsystems likely to generate relevant results. This selective approach extracts the essential subset of suppliers needed for effective search result generation, avoiding the waste of network bandwidth and processing resources on transmitting irrelevant results from all suppliers.
4Productivity
If the system uses historical data to predict supplier performance, then the routing efficiency is improved, but the system complexity increases
Solution Approach 1:
The system uses copying by storing historical outcome indicators that replicate past performance patterns. Instead of implementing complex real-time analysis of supplier capabilities, the system copies historical performance data and uses it to guide routing decisions, simplifying the system architecture while maintaining routing efficiency.
Data Source
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.