How fast does the web change? Does most of the content remain unchanged once it has been authored, or are the documents continuously updated? Do pages change a little or a lot? Is the extent of change correlated to any other property of the page? All of these questions are of interest to those who mine the web, including all the popular search engines, but few studies have been performed to date to answer them. One notable exception is a study by Cho and Garcia-Molina, who crawled a set of 720,000 pages on a daily basis over four months, and counted pages as having changed if their MD5 checksum changed. They found that 40 % of all web pages in their set changed within a week, and 23 % of those pages that fell into the.com domain changed daily. This paper expands on Cho and Garcia-Molina’s study, both in terms of coverage and in terms of sensitivity to change. We crawled a set of 150,836,209 HTML pages once every week, over a span of 11 weeks. For each page, we recorded a checksum of the page, and a feature vector of the words on the page, plus various other data such as the page length, the HTTP status code, etc. Moreover, we pseudo-randomly selected 0.1 % of all of our URLs, and saved the full text of each download of the corresponding pages. After completion of the crawl, we analyzed the degree of change of each page, and investigated which factors are correlated with change intensity. We found that the average degree of change varies widely across top-level domains, and that larger pages change more often and more severely than smaller ones. This paper describes the crawl and the data transformations we performed on the logs, and presents some statistical observations on the degree of change of different classes of pages.

We offer an overview of current Web search engine design. After introducing a generic search engine architecture, we examine each engine component in turn. We cover crawling, local Web page storage, indexing, and the use of link analysis for boosting search performance. The most common design and implementation techniques for each of these components are presented. For this presentation we draw from the literature and from our own experimental search engine testbed. Emphasis is on introducing the fundamental concepts and the results of several performance analyses we conducted to compare different designs.

This article presents a high-level discussion of some problems in information retrieval that are unique to web search engines. The goal is to raise awareness and stimulate research in these areas.

There have been recent interests in studying the "goal" behind a user's Web query, so that this goal can be used to improve the quality of a search engine's results. Previous studies have mainly focused on using manual query-log investigation to identify Web query goals. In this paper we study whether and how we can automate this goal-identification process. We first present our results from a human subject study that strongly indicate the feasibility of automatic query-goal identification. We then propose two types of features for the goal-identification task: user-click behavior and anchor-link distribution. Our experimental evaluation shows that by combining these features we can correctly identify the goals for 90% of the queries studied.

Paper Number 201 Many web documents (such as JAVA FAQs) are being replicated on the Internet. Often entire document collections (such as hyperlinked Linux manuals) are being replicated many times. In this paper, we make the case for identifying replicated documents and collections to improve web crawlers, archivers, and ranking functions used in search engines. The paper describes how to efficiently identify replicated documents and hyperlinked document collections. The challenge is to identify these replicas from an input data set of several tens of millions of web pages and several hundreds of gigabytes of textual data. We also present two real-life case studies where we used replication information to improve a crawler and a search engine. We report these results for a data set of 25 million web pages (about 150 gigabytes of HTML data) crawled from the web.

We compare several algorithms for identifying mirrored hosts on the World Wide Web. The algorithms operate on the basis of URL strings and linkage data: the type of information easily available from web proxies and crawlers. Identification of mirrored hosts can improve web-based information retrieval in several ways: First, by identifying mirrored hosts, search engines can avoid storing and returning duplicate documents. Second, several new information retrieval techniques for the Web make inferences based on the explicit links among hypertext documents -- mirroring perturbs their graph model and degrades performance. Third, mirroring information can be used to redirect users to alternate mirror sites to compensate for various failures, and can thus improve the performance of web browsers and proxies. # This work was presented at the Workshop on Organizing Web Space at the Fourth ACM Conference on Digital Libraries 1999. We evaluated 4 classes of "top-down" algorithms for detecting ...

This paper expands on a 1997 study of the amount and distribution of near-duplicate pages on the World Wide Web. We downloaded a set of 150 million web pages on a weekly basis over the span of 11 weeks. We then determined which of these pages are near-duplicates of one another, and tracked how clusters of near-duplicate documents evolved over time. We found that 29.2% of all web pages are very similar to other pages, and that 22.2% are virtually identical to other pages. We also found that clusters of near-duplicate documents are fairly stable: Two documents that are near-duplicates of one another are very likely to still be near-duplicates 10 weeks later. This result is of significant relevance to search engines: Web crawlers can be fairly confident that two pages that have been found to be near-duplicates of one another will continue to be so for the foreseeable future, and may thus decide to recrawl only one version of that page, or at least to lower the download priority of the other versions, thereby freeing up crawling resources that can be brought to bear more productively somewhere else.

Many objects, such as les, electronic messages, and web pages, contain overlapping content. Numerous past research projects have observed that one can compress one object relative to another one by computing the differences between the two, but these delta-encoding systems have almost invariably required knowledge of a specific relationship between them most commonly, two versions using the same name at different points in time. We consider cases in which this relationship is determined dynamically, by efficiently determining when a sufficient resemblance exists between two objects in a relatively large collection. We look at specific examples of this technique, namely web pages, email, and files in a file system, and evaluate the potential data reduction and the factors that influence this reduction. We find that delta-encoding using this resemblance detection technique can improve on simple compression by up to a factor of two, depending on workload, and that a small fraction of objects can potentially account for a large portion of these savings.

Aliasing occurs in Web transactions when requests containing different URLs elicit replies containing identical data payloads. Aliasing can cause cache misses, and there is reason to suspect that offthe -shelf Web authoring tools might increase aliasing on the Web. Existing research literature, however, says little about the prevalence of aliasing in user-initiated transactions or its impact on endto -end performance in large multi-level cache hierarchies.

We consider the problem of indexing high-dimensional data for answering (approximate) similarity-search queries. Similarity indexes prove to be important in a wide variety of settings: Web search engines desire fast, parallel, main-memory-based indexes for similarity search on text data; database systems desire disk-based similarity indexes for high-dimensional data, including text and images; peer-to-peer systems desire distributed similarity indexes with low communication cost. We propose an indexing scheme called LSH Forest which is applicable in all the above contexts. Our index uses the well-known technique of locality-sensitive hashing (LSH), but improves upon previous designs by (a) eliminating the different data-dependent parameters for which LSH must be constantly hand-tuned, and (b) improving on LSH’s performance guarantees for skewed data distributions while retaining the same storage and query overhead. We show how to construct this index in main memory, on disk, in parallel systems, and in peer-to-peer systems. We evaluate the design with experiments on multiple text corpora and demonstrate both the self-tuning nature and the superior performance of LSH Forest.