Elastic Search Cluster Query Template Relevancy
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Solution Overview
Problem
Traditional search platforms fail to provide relevant search results to individual users, as they do not consider user-specific parameters such as location, device type, search history, and current promotions, leading to ineffective conversions.
Innovation Solution
An elastic search cluster with a core search engine that processes data from multiple sources, creates data file indexes, and applies relevancy factors to search queries based on user-specific parameters, using query templates and a relevancy management portal to tailor search results.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional search platforms are used to match search queries to database, then search results are returned quickly, but the search results are not relevant to individual users because user-specific parameters are not considered
Solution Approach 1:
The patent introduces an elastic search cluster as an intermediary component between the traditional search platform and the database. This intermediary processes search queries by incorporating user-specific parameters (location, device type, search history, current promotions) to generate personalized search results, thereby improving relevancy without fundamentally redesigning the entire search platform architecture
Solution Approach 2:
The search platform is segmented into multiple independent components: the traditional search platform, the elastic search cluster, and the database. Each component performs a specific function - the traditional platform handles query reception, the elastic search cluster handles personalization processing, and the database handles data storage. This segmentation allows the system to improve relevancy through the specialized elastic search cluster while maintaining the simplicity of the original platform structure
2Productivity
If the same search results are provided for all users, then the search process is simple and fast, but conversion rates are low because results are not tailored to individual user needs
Solution Approach 1:
The elastic search cluster performs preliminary processing of user parameters and search queries before results are finalized. By pre-processing user-specific data (location, device type, search history, promotions) and incorporating it into the search process in advance, the system can generate personalized results without adding significant delay to the overall search time
Solution Approach 2:
The system dynamically changes search parameters based on user-specific factors. Instead of using fixed search parameters for all users, the elastic search cluster modifies parameters such as location filters, device-type filters, search-history weights, and promotion priorities to match each user's context, thereby improving conversion rates while maintaining efficient search processing
Data Source
AI summary
Techniques are disclosed to facilitate an improved data query system for an elastic search cluster. An ingestion framework receives data from several sources, indexes this data, which is then stored as indexed data files across several data nodes in the elastic search cluster. The system also utilizes a query template management system, which matches a query to a particular query template based upon various parameters. The matched query template specifies one or more relevancy factors that are applied to the query to cause the elastic search cluster to perform the search in a specific manner. In this way, the query templates allow for search results to be tailored in a highly customized manner based upon the desired characteristics that are used to match, or trigger, the specific query template. The relevancy factors can also be dynamically updated to improve the search results over time.