In this paper we study in what order a crawler should visit the URLs it has seen, in order to obtain more “important” pages first. Obtaining important pages rapidly can be very useful when a crawler cannot visit the entire Web in a reasonable amount of time. We define several importance metrics, ordering schemes, and performance evaluation measures for this problem. We also experimentally evaluate the ordering schemes on the Stanford University Web. Our results show that a crawler with a good ordering scheme can obtain important pages significantly faster than one without.

We present a method for searching in an image database using a query image that is similar to the intended target. The query image may be a hand-drawn sketch or a (potentially low-quality) scan of the image to be retrieved. Our searching algorithm makes use of multiresolution wavelet decompositions of the query and database images. The coefficients of these decompositions are distilled into small "signatures" for each image. We introduce an "image querying metric" that operates on these signatures. This metric essentially compares how many significant wavelet coefficients the query has in common with potential targets. The metric includes parameters that can be tuned, using a statistical analysis, to accommodate the kinds of image distortions found in different types of image queries. The resulting algorithm is simple, requires very little storage overhead for the database of signatures, and is fast enough to be performed on a database of 20,000 images at interactive rates (on standard...

Standard Web search services, though useful, are far from ideal. There are over a dozen different search services currently in existence, each with a unique interface and a database covering a different portion of the Web. As a result, users are forced to repeatedly try and retry their queries across different services. Furthermore, the services return many responses that are irrelevant, outdated, or unavailable, forcing the user to manually sift through the responses searching for useful information. This paper presents the MetaCrawler, a fielded Web service that represents the next level up in the information "food chain." The MetaCrawler provides a single, central interface for Web document searching. Upon receiving a query, the MetaCrawler posts the query to multiple search services in parallel, collates the returned references, and loads those references to verify their existence and to ensure that they contain relevant information. The MetaCrawler is sufficiently lightweight to r...

The MetaCrawler Softbot is a parallel Web search service that has been available at the University of Washington since June of 1995. It provides users with a single interface with which they can query popular general-purpose Web search services, such as Lycos[6] and AltaVista[1], and has some sophisticated features that allow it to obtain results of much higher quality than simply regurgitating the output from each search

This paper describes Mercator, a scalable, extensible web crawler written entirely in Java. Scalable web crawlers are an important component of many web services, but their design is not well-documented in the literature. We enumerate the major components of any scalable web crawler, comment on alternatives and tradeoffs in their design, and describe the particular components used in Mercator. We also describe Mercator’s support for extensibility and customizability. Finally, we comment on Mercator’s performance, which we have found to be comparable to that of other crawlers for which performance numbers have been published. 1

The current exponential growth of the Internet precipitates a need for new tools to help people cope with the volume of information. To complement recent work on creating searchable indexes of the World-Wide Web and systems for filtering incoming e-mail and Usenet news articles, we describe a system which helps users keep abreast of new and interesting information. Every day it presents a selection of interesting web pages. The user evaluates each page, and given this feedback the system adapts and attempts to produce better pages the following day. We present some early results from an AI programming class to whom this was set as a project, and then describe our current implementation. Over the course of 24 days the output of our system was compared to both randomly-selected and human-selected pages. It consistently performed better than the random pages, and was better than the human-selected pages half of the time. Introduction In recent years there has been a well-publicized expl...

Frequently a user's information needs are stored in the databases of multiple search engines. It is inconvenient and inefficient for an ordinary user to invoke multiple search engines and identify useful documents from the returned results. To support unified access to multiple search engines, a metasearch engine can be constructed. When a metasearch engine receives a query from a user, it invokes the underlying search engines to retrieve useful information for the user. Metasearch engines have other benefits as a search tool such as increasing the search coverage of the Web and improving the scalability of the search. In this article, we survey techniques that have been proposed to tackle several underlying challenges for building a good metasearch engine. Among the main challenges, the database selection problem is to identify search engines that are likely to return useful documents to a given query. The document selection problem is to determine what documents to retrieve from each identified search engine. The result merging problem is to combine the documents returned from multiple search engines. We will also point out some problems that need to be further researched.

HyPursuit is a new hierarchical network search engine that clusters hypertext documents to structure a given information space for browsing and search activities. Our content-link clustering algorithm is based on the semantic information embedded in hyperlink structures and document contents. HyPursuit admits multiple, coexisting cluster hierarchies based on different principles for grouping documents, such as the Library of Congress catalog scheme and automatically created hypertext clusters. HyPursuit's abstraction functions summarize cluster contents to support scalable query processing. The abstraction functions satisfy system resource limitations with controlled information loss. The result of query processing operations on a cluster summary approximates the result of performing the operations on the entire information space. We constructed a prototype system comprising 100 leaf World Wide Web sites and a hierarchy of 42 servers that route queries to the leaf sites. Experience with our system suggests that abstraction functions based on hypertext clustering can be used to construct meaningful and scalable cluster hierarchies. We are also encouraged by preliminary results on clustering based on both document contents and hyperlink structures.

The World-Wide Web (WWW) is an ever growing, distributed, non-administered, global information resource. It resides on the worldwide computer network and allows access to heterogeneous information: text, image, video, sound and graphic data. Currently, this wealth of information is difficult to mine. One can either manually, slowly and tediously navigate through the WWW or utilize indexes and libraries which are built by automatic search engines (called knowbots or robots). We have designed and are now implementing a high level SQL-like language to support effective and flexible query processing, which addresses the structure and content of WWW nodes and their varied sorts of data. Query results are intuitively presented and continuously maintained when desired. The language itself integrates new utilities and existing Unix tools (e.g. grep, awk). The implementation strategy is to employ existing WWW browsers and Unix tools to the extent possible. 1

Due to limited bandwidth, storage, and computational resources, and to the dynamic nature of the Web, search engines cannot index every Web page, and even the covered portion of the Web cannot be monitored continuously for changes. Therefore it is essential to develop effective crawling strategies to prioritize the pages to be indexed. The issue is even more important for topic-specific search engines, where crawlers must make additional decisions based on the relevance of visited pages. However, it is difficult to evaluate alternative crawling strategies because relevant sets are unknown and the search space is changing. We propose three different methods to evaluate crawling strategies. We apply the proposed metrics to compare three topic-driven crawling algorithms based on similarity ranking, link analysis, and adaptive agents.