Compression-Based Search Engine Query Evaluation
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing search engine technologies are inefficient in identifying query strings within search data due to resource-intensive pattern matching and rule-based mapping methods, which fail to capture variations in query strings effectively, leading to reduced accuracy and increased processing time.
Innovation Solution
The implementation of a compression-based evaluation method that sanitizes, corrects, lemmatizes, and compresses query strings and search data into a reduced encoding space, allowing for faster and more accurate identification of query strings by comparing distilled data using code matching and operator evaluation operations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If pattern matching and rule-based mapping methods are used to evaluate query strings, then the search engine can identify query strings in search data, but the processing time and resource consumption increase significantly
Solution Approach 1:
The patent transforms the search data and query strings from their original text form into compressed binary representations. This parameter change from text to compressed binary code dramatically reduces the complexity of pattern matching operations while maintaining the ability to accurately identify query strings, thereby resolving the contradiction between identification accuracy and processing time
Solution Approach 2:
The patent creates compressed copies of the original search data and query strings. Instead of performing pattern matching on the full text data, the system works with compressed binary representations that preserve the essential information needed for accurate query string identification while enabling faster processing
2Measurement precision
If pattern matching and rule-based mapping methods are used to evaluate query strings, then the search engine can identify query strings in search data, but the resource consumption increases
Solution Approach 1:
The transformation to compressed binary representation fundamentally changes the parameters of the data structure, reducing the amount of memory and computational resources required for pattern matching operations while maintaining identification accuracy
Solution Approach 2:
The patent segments the original text data into compressed binary components, allowing the system to process and compare only the essential features of the data rather than the entire text, thereby reducing resource consumption while preserving accuracy
3Adaptability or versatility
If traditional search methods are used, then the search engine can process search data, but it fails to capture variations in query strings effectively
Solution Approach 1:
The compressed binary representation system serves multiple functions: it enables accurate pattern matching, captures variations in query strings through the compression process, and maintains efficient processing. This universal approach resolves the contradiction between adaptability to variations and identification accuracy
Data Source
AI summary
A system described herein may provide a technique for the compression of query terms and search data against which the query terms may be evaluated. The compression may be dynamic, in that a quantity of bits used to compress the search data and query terms may be based on a quantity of unique characters included in a given query term. The compression may further include reducing the volume of search data by compressing entire words, that do not include any of the unique characters of the query term, to one particular code.