Multi-Tier Database Querying for Search Accuracy
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Solution Overview
Problem
Direct query searches in database management systems often fail to provide suitable search results due to user errors, misinformation, or mismatches between search strings and database records, leading to sub-optimal or non-matching results, which can necessitate manual searching, affecting timeliness and quality.
Innovation Solution
The implementation of a multi-tiered searching technique that uses keyword identification and relevance scoring, where the database management system generates queries with varying levels of restrictiveness, starting with a 'must' clause and progressing to 'should' clauses, and employs multiple rounds of querying to refine results based on relevance scores.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If direct query search is used, then search speed is fast, but search accuracy deteriorates due to user errors and mismatches
Solution Approach 1:
The system performs preliminary actions by generating multiple candidate queries with varying levels of restrictiveness before the final search execution. The multi-tiered query generation creates progressive query versions (from most to least restrictive) in advance, allowing the system to handle user errors and mismatches before actual search execution, thereby improving search accuracy without significantly increasing search time.
Solution Approach 2:
The system applies dynamics by making the query restrictiveness adjustable and adaptive. The multi-tiered query structure allows dynamic adjustment of search criteria strictness, enabling the system to transition between different query versions based on search results quality. This dynamic approach balances search accuracy and speed by automatically selecting the appropriate query restrictiveness level.
2Measurement precision
If multi-tiered querying is implemented, then search accuracy is improved, but system complexity increases
Solution Approach 1:
The system segments the query generation process into multiple distinct tiers or levels of restrictiveness. Each tier represents a separate query version with specific criteria strictness, allowing the complex search problem to be divided into manageable segments. This segmentation improves search accuracy by systematically exploring different query interpretations while keeping each individual query segment relatively simple.
Solution Approach 2:
The system changes query parameters systematically across different tiers, specifically adjusting the restrictiveness level and criteria strictness. By varying parameters like match threshold, required field coverage, and synonym expansion across query tiers, the system achieves improved search accuracy through parameter exploration without requiring fundamentally different search mechanisms, thus limiting the increase in system complexity.
3Measurement precision
If iterative query refinement is performed, then relevance of results is improved, but processing time increases
Solution Approach 1:
The system implements periodic action by executing queries in distinct rounds or iterations, where each round refines the search based on previous results. The multi-tiered querying approach performs periodic refinement cycles, alternating between executing queries at different restrictiveness levels and adjusting subsequent queries based on result quality. This periodic refinement improves result relevance while controlling processing time by structuring iterations efficiently.
Solution Approach 2:
The system performs preliminary query generation and ranking setup before actual iterative refinement begins. By pre-computing query tiers, candidate queries, and relevance criteria in advance, the system reduces the processing overhead during iterative refinement. This preliminary preparation allows subsequent iterative queries to focus only on result evaluation and adjustment, improving result relevance with minimal additional processing time.
Data Source
AI summary
Various examples are directed to systems and methods for identifying database records in a database table. A database management system receives a search request comprising a first set of strings associated with a first column of the database table and a second set of strings associated with a second column of the database table. The database management system selects a set of first column keywords using the first set of strings and executes a first tier query at the database table. Responsive to determining that no database record returned by the first tier query has a relevance score greater than a threshold value, the database management system executes a second tier query at the database table.