Routing Memento Requests Using Binary Classifiers

TL;DR

This paper proposes using binary classifiers based on cached content to efficiently route Memento requests across multiple web archives, significantly reducing requests and response times while maintaining high recall.

Contribution

It introduces a novel approach of archive-specific classifiers for query routing, improving efficiency in Memento aggregators over heuristic methods.

Findings

Classifiers reduce requests by 77% compared to brute force

Response times decrease by 42%

Recall remains high at 0.847

Abstract

The Memento protocol provides a uniform approach to query individual web archives. Soon after its emergence, Memento Aggregator infrastructure was introduced that supports querying across multiple archives simultaneously. An Aggregator generates a response by issuing the respective Memento request against each of the distributed archives it covers. As the number of archives grows, it becomes increasingly challenging to deliver aggregate responses while keeping response times and computational costs under control. Ad-hoc heuristic approaches have been introduced to address this challenge and research has been conducted aimed at optimizing query routing based on archive profiles. In this paper, we explore the use of binary, archive-specific classifiers generated on the basis of the content cached by an Aggregator, to determine whether or not to query an archive for a given URI. Our results turn out to be readily applicable and can help to significantly decrease both the number of requests and the overall response times without compromising on recall. We find, among others, that classifiers can reduce the average number of requests by 77% compared to a brute force approach on all archives, and the overall response time by 42% while maintaining a recall of 0.847.