Search Result Processing With Deferred Data Computation
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Solution Overview
Problem
Search systems face challenges in efficiently processing search requests due to increased complexity and volume, with dynamic computation requirements straining hardware resources, making it difficult to maintain high quality of service standards.
Innovation Solution
A method where search engines prioritize computing essential data pieces initially, deferring non-essential data piece computation until a follow-up request, optimizing resource usage and response times.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If all data pieces (essential and non-essential) are computed and returned in the initial search results, then the client receives complete information, but the computation resources and response time are excessively consumed
Solution Approach 1:
The patent segments search result data into two categories: essential data pieces and non-essential data pieces. Essential data pieces are computed and returned immediately in the initial search results, while non-essential data pieces are deferred to be computed later based on follow-up requests. This segmentation allows the system to provide complete information ultimately while reducing initial computation time and resource consumption.
2Loss of information
If all data pieces are computed in the initial search results, then information completeness is maintained, but computation resources are excessively consumed
Solution Approach 1:
The patent segments search result data into essential and non-essential data pieces. Essential data pieces require computation resources and are computed immediately, while non-essential data pieces are deferred to later computation based on follow-up requests. This segmentation reduces initial computation resource consumption while maintaining the ability to provide complete information when needed.
3Productivity
If non-essential data pieces are omitted from initial search results, then resource efficiency and response time improve, but information completeness is reduced
Solution Approach 1:
The patent implements a dynamic approach where the completeness of search results evolves over time. Initially, only essential data pieces are provided to ensure fast response and resource efficiency. When the client sends follow-up requests indicating interest in specific search results, the system dynamically computes and provides the previously omitted non-essential data pieces, thereby adapting information completeness to actual client needs.
4Reliability
If the search engine computes all data pieces for every search request, then quality of service in terms of information completeness is maintained, but the system cannot handle increased traffic volume
Solution Approach 1:
The patent segments computation tasks into immediate essential data computation and deferred non-essential data computation. This segmentation allows the search engine to handle high traffic volume by quickly processing essential data for all requests while only computing non-essential data for a subset of requests that generate follow-up requests, thereby maintaining quality of service while increasing productivity.
Data Source
AI summary
A search engine receives a search request and determines a number of initial search results. The initial search results comprise at least one essential data piece and omit at least one non-essential data piece. Later on, a follow-up search request from the client is received which indicates at least one of the initial search results. The search engine computes at least one non-essential data piece for each initial search result indicated in the follow-up search request based on a number of dynamic computation rules and returns the computed at least one non-essential data piece for each initial search result to the client.