Routing Memento Requests Using Binary Classifiers
Nicolas J. Bornand, Lyudmila Balakireva, Herbert Van de Sompel

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…
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