Search Request Processing With Cached Results and ML Completion
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Solution Overview
Problem
Existing search systems face inefficiencies in handling search requests due to varying response times, processing resources, and data validity across multiple data sources, particularly when utilizing caches and machine learning models for search result generation.
Innovation Solution
A method that utilizes a cache for incomplete search results with first data fields and generates second data fields using a machine learning model, such as a deep neural network or gradient boosting tree, to assemble complete search results efficiently, optimizing tree traversal and resource usage.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If complete search results are generated using multiple data sources for each search request, then data validity and freshness are improved, but response time and processing resource consumption increase
Solution Approach 1:
The system performs preliminary actions by pre-computing and caching incomplete search results containing first data fields before actual search requests arrive. When a search request comes in, the system retrieves these pre-computed results from cache and only generates the remaining second data fields using machine learning models, rather than computing everything from scratch. This preliminary preparation significantly reduces response time while maintaining data validity.
Solution Approach 2:
The search result generation process is segmented into two distinct parts: first data fields that are pre-computed and cached, and second data fields that are generated on-demand using machine learning models. This segmentation allows the system to handle different data fields differently - storing static or slowly changing fields in advance while dynamically generating fields that require real-time computation, thus balancing between response time and data freshness.
2Reliability
If complete search results are generated using multiple data sources for each search request, then data freshness is improved, but processing resource consumption increases
Solution Approach 1:
The system performs preliminary actions by pre-computing and caching incomplete search results containing first data fields before actual search requests arrive. When a search request comes in, the system retrieves these pre-computed results from cache and only generates the remaining second data fields using machine learning models, rather than computing everything from scratch. This preliminary preparation significantly reduces response time while maintaining data validity.
Solution Approach 2:
The search result generation process is segmented into two distinct parts: first data fields that are pre-computed and cached, and second data fields that are generated on-demand using machine learning models. This segmentation allows the system to handle different data fields differently - storing static or slowly changing fields in advance while dynamically generating fields that require real-time computation, thus balancing between response time and data freshness.
3Loss of time
If cached incomplete search results are used to reduce processing time, then response time is improved, but data completeness deteriorates
Solution Approach 1:
The search result generation process is segmented into two distinct parts: first data fields that are pre-computed and cached, and second data fields that are generated on-demand using machine learning models. This segmentation allows the system to handle different data fields differently - storing static or slowly changing fields in advance while dynamically generating fields that require real-time computation, thus balancing between response time and data freshness.
Solution Approach 2:
Machine learning models serve as intermediaries that bridge the gap between cached incomplete search results and complete search results. The ML models take the cached first data fields as input and generate the missing second data fields, effectively completing the search results without requiring access to the original multiple data sources. This intermediary approach restores data completeness while maintaining the speed benefits of caching.
4Measurement precision
If machine learning models are used to generate second data fields, then data accuracy is improved, but processing complexity increases
Solution Approach 1:
The search result generation process is segmented into two distinct parts: first data fields that are pre-computed and cached, and second data fields that are generated on-demand using machine learning models. This segmentation allows the system to handle different data fields differently - storing static or slowly changing fields in advance while dynamically generating fields that require real-time computation, thus balancing between response time and data freshness.
Data Source
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AI summary
Method, systems and computer programs for handling search requests at a search platform are provided. The search platform determines, using a cache with a number of incomplete search results, one or more of the incomplete search results with first data fields that correspond to the least one search parameter. For each determined incomplete search result, the search platform generates at least one second data field using a machine learning model. The at least one second data field corresponds to at least one search parameter and the at least one first data field of each determined incomplete search result. The search platform assembles a number of completed search results on the basis of the determined incomplete search results and the generated at least one second data field and returns at least one of the completed search results.