Wildcard Token Deduplication for Database Search Optimization
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Solution Overview
Problem
Existing search expressions in relational databases are inefficient due to the presence of physically and logically duplicate wildcard tokens, which increase computational expense, particularly when using SQL LIKE or ILIKE operations for wildcard tokens starting with '*', leading to suboptimal search performance.
Innovation Solution
A method is introduced to separate keywords into word and wildcard tokens, where inferred wildcard tokens are removed based on their equivalence to word or other wildcard tokens, optimizing search expressions by eliminating duplicates and enhancing performance through inferential relationships between tokens.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If wildcard tokens are used in search expressions, then search flexibility is improved, but computational expense increases
Solution Approach 1:
The patent extracts and removes duplicate wildcard tokens from the search expression before execution. By identifying and eliminating redundant wildcard tokens (both physically duplicate and logically duplicate), the system reduces the number of tokens that need to be processed during search operations, thereby decreasing computational expense while preserving search flexibility through the retention of unique wildcard tokens
Solution Approach 2:
The patent performs preliminary processing of the search expression by separating tokens into word tokens and wildcard tokens, and then removing duplicates before the actual search execution. This preliminary action of token separation and deduplication reduces the workload for subsequent search operations, achieving lower computational expense without compromising the adaptability of wildcard-based searches
2Productivity
If duplicate wildcard tokens are removed from search expressions, then search performance is improved, but query processing complexity increases
Solution Approach 1:
The patent segments the search expression into distinct word tokens and wildcard tokens, and further divides wildcard tokens into physically duplicate and logically duplicate categories. This segmentation enables targeted removal of specific token types while preserving others, improving search performance through reduced token count while managing processing complexity through systematic categorization
Solution Approach 2:
The patent changes the parameter of token representation by converting wildcard tokens into a standardized format with wildcard characters, enabling systematic identification and removal of duplicates. This parameter change allows for efficient duplicate detection and removal while maintaining the essential search functionality, thus improving performance without excessive complexity increase
Data Source
AI summary
In one example in accordance with the present disclosure, a method may include separating a list of keywords into a set of word tokens and a set of wildcard tokens. The method may also include removing each wildcard token in the set of wildcard tokens that is inferred by at least one word token in the set of word tokens and removing each wildcard token in the set of wildcard tokens that is inferred by at least one other wildcard token in the set of wildcard tokens. The method may also include executing a search query comprising a new list of keywords that includes each wildcard token not removed from the set of wildcard tokens.