Query-Based Sampling using Snippets

Query-based sampling is a commonly used approach to model the content of servers. Conventionally, queries are sent to a server and the documents in the search results returned are downloaded in full as representation of the server’s content. We present an approach that uses the document snippets in the search results as samples instead of downloading the entire documents. We show this yields equal or better modeling performance for the same bandwidth consumption depending on collection characteristics, like document length distribution and homogeneity. Query-based sampling using snippets is a useful approach for real-world systems, since it requires no extra operations beyond exchanging queries and search results

How Much of the Web Is Archived?

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.Comment: This is the long version of the short paper by the same title published at JCDL'11. 10 pages, 5 figures, 7 tables. Version 2 includes minor typographical correction

Expanding the Usage of Web Archives by Recommending Archived Webpages Using Only the URI

Web archives are a window to view past versions of webpages. When a user requests a webpage on the live Web, such as http://tripadvisor.com/where_to_t ravel/, the webpage may not be found, which results in an HyperText Transfer Protocol (HTTP) 404 response. The user then may search for the webpage in a Web archive, such as the Internet Archive. Unfortunately, if this page had never been archived, the user will not be able to view the page, nor will the user gain any information on other webpages that have similar content in the archive, such as the archived webpage http://classy-travel.net. Similarly, if the user requests the webpage http://hokiesports.com/football/ from the Internet Archive, the user will only find the requested webpage, and the user will not gain any information on other webpages that have similar content in the archive, such as the archived webpage http://techsideline.com. In this research, we will build a model for selecting and ranking possible recommended webpages at a Web archive. This is to enhance both HTTP 404 responses and HTTP 200 responses by surfacing webpages in the archive that the user may not know existed. First, we detect semantics in the requested Uniform Resource Identifier (URI). Next, we classify the URI using an ontology, such as DMOZ or any website directory. Finally, we filter and rank candidates based on several features, such as archival quality, webpage popularity, temporal similarity, and content similarity. We measure the performance of each step using different techniques, including calculating the F1 to measure of different tokenization methods and the classification. We tested the model using human evaluation to determine if we could classify and find recommendations for a sample of requests from the Internet Archive’s Wayback Machine access log. Overall, when selecting the full categorization, reviewers agreed with 80.3% of the recommendations, which is much higher than “do not agree” and “I do not know”. This indicates the reviewer is more likely to agree on the recommendations when selecting the full categorization. But when selecting the first level only, reviewers only agreed with 25.5% of the recommendations. This indicates that having deep level categorization improves the performance of finding relevant recommendations

Web Archive Services Framework for Tighter Integration Between the Past and Present Web

Web archives have contained the cultural history of the web for many years, but they still have a limited capability for access. Most of the web archiving research has focused on crawling and preservation activities, with little focus on the delivery methods. The current access methods are tightly coupled with web archive infrastructure, hard to replicate or integrate with other web archives, and do not cover all the users\u27 needs. In this dissertation, we focus on the access methods for archived web data to enable users, third-party developers, researchers, and others to gain knowledge from the web archives. We build ArcSys, a new service framework that extracts, preserves, and exposes APIs for the web archive corpus. The dissertation introduces a novel categorization technique to divide the archived corpus into four levels. For each level, we will propose suitable services and APIs that enable both users and third-party developers to build new interfaces. The first level is the content level that extracts the content from the archived web data. We develop ArcContent to expose the web archive content processed through various filters. The second level is the metadata level; we extract the metadata from the archived web data and make it available to users. We implement two services, ArcLink for temporal web graph and ArcThumb for optimizing the thumbnail creation in the web archives. The third level is the URI level that focuses on using the URI HTTP redirection status to enhance the user query. Finally, the highest level in the web archiving service framework pyramid is the archive level. In this level, we define the web archive by the characteristics of its corpus and building Web Archive Profiles. The profiles are used by the Memento Aggregator for query optimization