Category-Specific Data Scrapers for Web Page Extraction
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Solution Overview
Problem
Existing data scrapers often rely excessively on HTML structure, leading to erroneous data extraction when the structure changes, as they fail to adapt to different web page structures, resulting in inefficient and inaccurate data extraction across various web pages.
Innovation Solution
A server system that categorizes data based on URL patterns, structural patterns, or metatag patterns to determine the appropriate scraper configuration for extracting data from web pages or native applications, ensuring accurate data extraction by using category-specific scrapers tailored to each data category.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If a single scraper is used for different web pages, then device complexity is reduced, but data extraction reliability deteriorates due to erroneous extraction from structural changes
Solution Approach 1:
The patent segments the scraping system into multiple category-specific scrapers, each tailored to handle specific data categories (e.g., e-commerce, news, social media). This segmentation allows each scraper to be optimized for its specific domain while maintaining overall system reliability, resolving the contradiction between simplicity and reliability.
Solution Approach 2:
The system dynamically changes scraper parameters based on URL patterns and data categories. By detecting the category through URL analysis and adjusting scraper configurations accordingly, the system achieves high reliability without requiring completely separate scrapers for each category, thus balancing complexity and reliability.
2Measurement precision
If custom scrapers are built for each web page structure, then data extraction precision is improved, but loss of time increases due to time-consuming customization
Solution Approach 1:
The patent creates universal scraper templates that can handle multiple data categories through configuration rather than custom coding. These universal scrapers are configured based on detected URL patterns and category types, achieving high extraction precision without the time cost of building custom scrapers from scratch for each webpage structure.
Solution Approach 2:
The system performs preliminary category detection through URL pattern matching before scraping. This preliminary action determines which pre-configured scraper template to apply, eliminating the need for time-consuming custom scraper construction while maintaining high precision through category-optimized extraction parameters.
3Ease of operation
If scrapers rely on HTML structure, then ease of operation is improved, but reliability deteriorates when page structure changes
Solution Approach 1:
The system incorporates feedback mechanisms that detect URL patterns and page structure characteristics during scraping operations. This feedback is used to dynamically adjust scraper configurations or switch between different scraper templates, maintaining reliability despite HTML structure changes while keeping operations simple through automated adaptation.
Solution Approach 2:
The scraping system is made dynamic by automatically detecting data categories through URL analysis and adapting scraper behavior accordingly. Instead of relying on static HTML structure assumptions, the system dynamically selects and configures appropriate scrapers based on detected patterns, maintaining both ease of operation and reliability under varying structural conditions.
Data Source
AI summary
A server configured to receive data corresponding with a web page and/or a native application, the data comprising a uniform resource locator (URL); compare the URL included in the data with URL patterns stored in a category data store; determine a data category of the data based on the comparison; determine a data scraper based on the data category, the data scraper comprising a scraper configuration that enables the data scraper to extract information from the web page and/or the native application; scrape the web page and/or the native application by extracting information from the web page and/or the native application using the data scraper; store the extracted information in one or more data stores; used the stored information to provide search results and/or card objects.