In this paper we present an analysis of an AltaVista Search Engine query log consisting of approximately 1 billion entries for search requests over a period of six weeks. This represents almost 285 million user sessions, each an attempt to fill a single information need. We present an analysis of individual queries, query duplication, and query sessions. We also present results of a correlation analysis of the log entries, studying the interaction of terms within queries. Our data supports the conjecture that web users differ significantly from the user assumed in the standard information retrieval literature. Specifically, we show that web users type in short queries, mostly look at the first 10 results only, and seldom modify the query. This suggests that traditional information retrieval techniques may not work well for answering web search requests. The correlation analysis showed that the most highly correlated items are constituents of phrases. This result indicates it may be useful for search engines to consider search terms as parts of phrases even if the user did not explicitly specify them as such.

In this paper we introduce a programming language for Web document processing called WebL. WebL is a high level, object-oriented scripting language that incorporates two novel features: service combinators and a markup algebra. Service combinators are language constructs that provide reliable access to web services by mimicking a web surfer's behavior when a failure occurs while retrieving a page. The markup algebra extracts structured and unstructured values from pages for computation, and is based on algebraic operations on sets of markup elements. WebL is used to quickly build and experiment with custom web crawlers, meta-search engines, page transducers, shopping robots, etc.

We motivate the use of desktop assistants in the context of web surfing and show how such a too1 may be used to support activities in both cooperative and personal surfing. , By cooperative surfing we mean surfing by a community of users who choose to cooperatively and asynchronously build up knowledge structures relevant to their group. Specifically, we describe the design of an assistant called Vistabar, which lives on the Windows desktop and operates on the currently active web browser. Vistabar instances working for individual users support the authoring of annotations and shared bookmark hierarchies, and work with profiles of community interests to make findings highly available. Thus, they support a form of community memory. Visfabar also serves as a form of personal memory by indexing pages the user sees to assist in recall. We present rationale for the assistant's design, describe roles it could play to support surfing (including those mentioned above), tid suggest efficient implementation strategies where appropriate.

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