URL Canonicalization via Content Fingerprint Entropy Analysis
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Solution Overview
Problem
The challenge lies in efficiently managing Uniform Resource Locators (URLs) that point to the same online content, as web crawlers download identical content multiple times, wasting resources due to the organic and heterogeneous nature of the Internet, where many distinct URLs refer to the same content.
Innovation Solution
A system that determines the relevance of URL parameters by calculating entropies of fingerprint values and parameter values, identifying irrelevant parameters and generating canonical URLs to avoid duplicate downloads by equating new URLs with already-downloaded content.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If web crawlers download content from all discovered URLs, then complete content coverage is achieved, but duplicate content downloads waste bandwidth and resources
Solution Approach 1:
The system performs preliminary fingerprinting of content files before downloading them. By calculating fingerprints (hashes) of the content data and comparing them against existing fingerprints in the database, the system can identify duplicate content in advance and avoid downloading it, thus preventing bandwidth waste while maintaining complete content coverage.
Solution Approach 2:
Instead of downloading and storing duplicate content files, the system creates and stores compact fingerprint copies (hash values) of content files. These fingerprints serve as unique identifiers that allow the system to recognize and skip duplicate content without occupying storage space or consuming bandwidth for redundant downloads.
2Productivity
If multiple distinct URLs pointing to the same content are treated as separate resources, then all URL variations are indexed, but redundant downloads occur
Solution Approach 1:
The system replaces complex URL-based content identification with simplified fingerprint-based identification. Instead of tracking multiple URL variations and their relationships, the system uses content fingerprints as universal identifiers, dramatically simplifying download management while maintaining complete indexing of all unique content.
Solution Approach 2:
The system changes the identification parameter from URL strings (which vary for the same content) to content fingerprints (which are identical for the same content). This parameter transformation allows the system to naturally group and deduplicate content regardless of URL variations, reducing management complexity.
3Reliability
If high bandwidth content like video files is downloaded from multiple URLs, then all references are captured, but resource waste increases significantly
Solution Approach 1:
The system performs preliminary fingerprint comparison before initiating downloads of high bandwidth content. By checking whether a content fingerprint already exists in the database, the system can prevent redundant downloads of large files like videos, thus capturing all content references while minimizing data transfer volume.
Solution Approach 2:
The system stores compact fingerprint copies of high bandwidth content instead of the content itself. This allows the system to recognize and skip duplicate high bandwidth content downloads while maintaining complete tracking of all content references, significantly reducing data transfer volume.
Data Source
AI summary
A computer-implemented method includes receiving a plurality of uniform resource locators (URLs), where the URLs identify content files, and where the URLs include at least one parameter. Fingerprints of the content files are identified. A first entropy of values of the fingerprints conditional on values of a first parameter is determined, and a second entropy of values of the first parameter conditional on values of the fingerprints is determined. It is determined that the first parameter is irrelevant to the identification of a unique content file by the URLs based, at least in part, on the first and second entropy values.