Although the Internet Archive's Wayback Machine is the largest and most well-known web archive, there have been a number of public web archives that have emerged in the last several years. With varying resources, audiences and collection development policies, these archives have varying levels of overlap with each other. While individual archives can be measured in terms of number of URIs, number of copies per URI, and intersection with other archives, to date there has been no answer to the question "How much of the Web is archived?" We study the question by approximating the Web using sample URIs from DMOZ, Delicious, Bitly, and search engine indexes; and, counting the number of copies of the sample URIs exist in various public web archives. Each sample set provides its own bias. The results from our sample sets indicate that range from 35%-90% of the Web has at least one archived copy, 17%-49% has between 2-5 copies, 1%-8% has 6-10 copies, and 8%-63% has more than 10 copies in public web archives. The number of URI copies varies as a function of time, but no more than 31.3% of URIs are archived more than once per month.

Abstract. The Memento aggregator currently polls every known public web archive when serving a request for an archived web page, even though some web archives focus on only specific domains and ignore the others. Similar to query routing in distributed search, we investigate the impact on aggregated Memento TimeMaps (lists of when and where a web page was archived) by only sending queries to archives likely to hold the archived page. We profile twelve public web archives using data from a variety of sources (the web, archives' access logs, and full-text queries to archives) and discover that only sending queries to the top three web archives (i.e., a 75% reduction in the number of queries) for any request produces the full TimeMaps on 84% of the cases.

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.

The Memento aggregator currently polls every known public web archive when serving a request for an archived web page, even though some web archives focus on only specific domains and ignore the others. Similar to query routing in distributed search, we investigate the impact on aggregated Memento TimeMaps (lists of when and where a web page was archived) by only sending queries to archives likely to hold the archived page. We profile twelve public web archives using data from a variety of sources (the web, archives' access logs, and full-text queries to archives) and discover that only sending queries to the top three web archives (i.e., a 75% reduction in the number of queries) for any request produces the full TimeMaps on 84% of the cases.

Thumbnails of archived web pages as they appear in common browsers such as Firefox or Chrome can be useful to convey the nature of a web page and how it has changed over time. However, creating thumbnails for all archived web pages is not feasible for large collections, both in terms of time to create the thumbnails and space to store them. Furthermore, at least for the purposes of initial exploration and collection understanding, people will likely only need a few dozen thumbnails and not thousands. In this paper, we develop different algorithms to optimize the thumbnail creation procedure for web archives based on information retrieval techniques. We study different features based on HTML text that correlate with changes in rendered thumbnails so we can know in advance which archived pages to use for thumbnails. We find that SimHash correlates with changes in the thumbnails (ρ = 0.59, p < 0.005). We propose different algorithms for thumbnail creation suitable for different applications, reducing the number of thumbnails to be generated to 9% -27% of the total size.