The Internet Archive's (IA) Wayback Machine is the largest and oldest public web archive and has become a significant repository of our recent history and cultural heritage. Despite its importance, there has been little research about how it is discovered and used. Based on web access logs, we analyze what users are looking for, why they come to IA, where they come from, and how pages link to IA. We find that users request English pages the most, followed by the European languages. Most human users come to web archives because they do not find the requested pages on the live web. About 65 of the requested archived pages no longer exist on the live web. We find that more than 82 of human sessions connect to the Wayback Machine via referrals from other web sites, while only 15 of robots have referrers. Most of the links (86 ) from websites are to individual archived pages at specific points in time, and of those 83 no longer exist on the live web.
* Memento Terminology In this section, we explain the terminology we adopt in the rest of the paper. Memento @cite_16 is an HTTP protocol extension which enables time travel on the web by linking the current resources with their prior state. Memento defines the following terms: URI-R identifies the original resource. It is the resource as it used to appear on the live web. A URI-R may have 0 or more mementos (URI-Ms). URI-M identifies an archived snapshot of the URI-R at a specific datetime, which is called Memento-Datetime, e.g., URI-M @math = URI-R @math . URI-T identifies a TimeMap, a resource that provides a list of mementos (URI-Ms) for a URI-R with their Memento-Datetimes, e.g., @math . Although we use Memento terminology, the logs we analyze are from the Internet Archive's Wayback Machine and not the Memento API.
@cite_16
1510484544 2417822008 2951582856 2218908741
The Web is ephemeral. Many resources have representations that change over time, and many of those representations are lost forever. A lucky few manage to reappear as archived resources that carry their own URIs. For example, some content management systems maintain version pages that reect a frozen prior state of their changing resources. Archives recurrently crawl the web to obtain the actual representation of resources, and subsequently make those available via special-purpose archived resources. In both cases, the archival copies have URIs that are protocolwise disconnected from the URI of the resource of which they represent a prior state. Indeed, the lack of temporal capabilities in the most common Web protocol, HTTP, prevents getting to an archived resource on the basis of the URI of its original. This turns accessing archived resources into a signicant discovery challenge for both human and software agents, which typically involves following a multitude of links from the original to the archival resource, or of searching archives for the original URI. This paper proposes the protocol-based Memento solution to address this problem, and describes a proof-of-concept experiment that includes major servers of archival content, including Wikipedia and the Internet Archive. The Memento solution is based on existing HTTP capabilities applied in a novel way to add the temporal dimension. The result is a framework in which archived resources can seamlessly be reached via the URI of their original: protocol-based time travel for the Web. 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. In this paper we present the results of a study into the persistence and availability of web resources referenced from papers in scholarly repositories. Two repositories with different characteristics, arXiv and the UNT digital library, are studied to determine if the nature of the repository, or of its content, has a bearing on the availability of the web resources cited by that content. Memento makes it possible to automate discovery of archived resources and to consider the time between the publication of the research and the archiving of the referenced URLs. This automation allows us to process more than 160000 URLs, the largest known such study, and the repository metadata allows consideration of the results by discipline. The results are startling: 45 (66096) of the URLs referenced from arXiv still exist, but are not preserved for future generations, and 28 of resources referenced by UNT papers have been lost. Moving forwards, we provide some initial recommendations, including that repositories should publish URL lists extracted from papers that could be used as seeds for web archiving systems. With the proliferation of public web archives, it is becoming more important to better profile their contents, both to understand their immense holdings as well as support routing of requests in the Memento aggregator. To save time, the Memento aggregator should only poll the archives that are likely to have a copy of the requested URI. Using the CDX files produced after crawling, we can generate profiles of the archives that summarize their holdings and can be used to inform routing of the Memento aggregator’s URI requests. Previous work in profiling ranged from using full URIs (no false positives, but with large profiles) to using only top-level domains (TLDs) (smaller profiles, but with many false positives). This work explores strategies in between these two extremes. In our experiments, we gained up to 22 routing precision with less than 5 relative cost as compared to the complete knowledge profile without any false negatives. With respect to the TLD-only profile, the registered domain profile doubled the routing precision, while complete hostname and one path segment gave a five fold increase in routing precision.