You are provided with the abstract of a scientific paper and the abstracts of the papers it references. Your task is to write a Related Work section for the main paper. This section should concisely summarize and integrate the key points from the cited abstracts, reflecting how they relate to the main paper's contributions and context. In your summary, use numerical citation markers like [1], [2], etc., to refer to the corresponding papers.

Main Abstract:
In this work we proposed MementoMap, a flexible and adaptive framework to summarize holdings of a web archive efficiently. We described a simple, yet extensible, file format suitable for MementoMap. We used the complete index of the Arquivo.pt comprising 5B mementos (archived web pages files) to understand the nature and shape of its holdings. We generated MementoMaps with varying amount of detail from its HTML pages that have an HTTP status code of 200 OK. Additionally, we designed a single-pass, memory-efficient, and parallelization-friendly algorithm to compact a large MementoMap into a small one and an in-file binary search method for efficient lookup. We analyzed more than three years of MemGator (a Memento aggregator) logs to understand the response behavior of 14 public web archives. We evaluated MementoMaps by measuring their Accuracy using 3.3M unique URIs from MemGator logs. We found that a MementoMap of less than 1.5 Relative Cost (as compared to the comprehensive listing of all the unique original URIs) can correctly identify the presence or absence of 60 of the lookup URIs in the corresponding archive while maintaining 100 Recall (i.e., zero false negatives).

Cited Abstract(s):
[1]: 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.
implemented a different approach for Memento routing by building binary classifiers from LANL's Time Travel aggregator cache data [1] . They analyzed responses from various archives in the aggregator's cache over a period of time to learn about the holdings of different archives. They reported a 77 approaches can be categorized as usage-based profiling in which access logs or caches are used to observe what people were looking for in archives and which of those lookups had a hit or miss in the past. While usage-based profiling can be useful for Memento lookup routing, it may not give the real picture of archives' holdings, producing both false negatives and false positives https: groups.google.com forum #!topic memento-dev YE4rt6L5ICg .