Pattern-Based Data Extraction System for Web Scraping
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Solution Overview
Problem
Current web crawlers and scrapers either gather excessive information or miss targeted data due to inefficient search methods, leading to high bandwidth usage and costs, and manual examination of large datasets is inefficient, especially for large amounts of data with repeated structures.
Innovation Solution
A system that identifies and extracts specific data elements from repeated patterns in network communication, such as product search results, by analyzing the structure of the data and using a cascading style sheet to extract related information for each product, reducing the need for manual examination and processing resources.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If web crawlers gather all available information from a target content source, then the amount of information gathered is maximized, but the information becomes unfiltered and unnecessarily large
Solution Approach 1:
The patent extracts only the necessary targeted information from the web page by identifying specific data elements within repeated patterns, rather than gathering all available information. This is achieved through analyzing the page structure, detecting repeated patterns, and selectively extracting only the required data elements, thus eliminating unnecessary information filtering steps.
Solution Approach 2:
The system performs preliminary analysis of the web page structure to identify repeated patterns and their associated data elements before actual data extraction. By pre-processing the page to understand its structure and pattern repetition, the system prepares extraction rules in advance, making the subsequent information gathering efficient and targeted without requiring post-processing filtering.
2Measurement precision
If web crawlers search for targeted information using specific search methods, then the extraction precision is improved, but other types of information may be missed
Solution Approach 1:
The patent implements a universal extraction approach that can handle multiple types of information through a single pattern-based framework. By identifying repeated patterns in the web page structure and extracting data elements based on their positions within these patterns, the system can adapt to extract different types of information (product names, prices, descriptions, etc.) from the same structural template, thus achieving both precision and versatility.
Solution Approach 2:
The extraction system dynamically adapts to different web page structures by automatically analyzing the repeated patterns present in each page. Rather than using fixed search methods, the system adjusts its extraction rules based on the detected pattern structure, allowing it to precisely extract targeted information while maintaining the ability to capture various information types from different page layouts and formats.
3Measurement precision
If manual examination is used to process large datasets, then the extraction accuracy is maintained, but the processing efficiency decreases significantly
Solution Approach 1:
The patent implements an automated system that performs data extraction without requiring manual examination. The system self-analyzes the web page structure, automatically detects repeated patterns, identifies data element positions within these patterns, and extracts the required information autonomously. This self-service approach maintains extraction accuracy through structured pattern matching while eliminating the time-consuming manual processing step entirely.
4Quantity of substance
If all information is gathered from network sources, then the data completeness is improved, but the bandwidth usage and costs increase
Solution Approach 1:
The patent extracts only the specific data elements needed from the web page by analyzing the repeated pattern structure and identifying the positions of required information. Instead of downloading and processing all information, the system targetedly extracts only necessary data elements (such as product names, prices, and descriptions) based on their positions within detected repeated patterns, significantly reducing bandwidth usage while maintaining data completeness for the required fields.
Data Source
AI summary
Techniques for extracting similar group elements are described. In one embodiment, a received communication is analyzed for repeating patterns in the elements within the communication. An input may be received via a user interface identifying a particular element of the received communication. A system may then identify a particular position within a repeating pattern that is associated with the particular element. Every element within the communication that is in the same position within the repeating pattern may then be identified, stored, or output in a specified or preselected format. Various embodiments may account for multi-page response communications, various pattern recognition techniques, and automated or user-assisted systems.