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  \textbf{\Large{\textsc{STANFORD UNIVERSITY}}}\\[5pt]
  \textbf{\Large{\textsc{DEPARTMENT OF STATISTICS}}}\\[5pt]
  \Large{\textsc{DEPARTMENTAL SEMINAR}}
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  4:15 p.m., Tuesday, November 13, 2007\\
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  Sequoia Hall Room 200\\
  (Cookies at 3:45 in 1st Floor Lounge)
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  \textsl{Carrie Grimes} \\
  Google\\
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  \subsection*{Estimation of Web Page Change Rates}
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\noindent

Search engines strive to maintain a "current" repository of all pages
on the web to index for user queries. However, crawling all pages all
the time is costly and inefficient: many small websites don't support
that much load, and while some pages change very rapidly, others don't change at all.
As a result, estimated frequency of change is often used to decide
how often a web page needs to be crawled. Here we consider a Poisson
process model for the number of state changes of a page, where a
crawler samples the page at some known time interval and observes
whether or not the page has changed in during that interval
from which a Maximum Likelihood Estimator is calculated. We examine
the performance of the MLE in a practical setting where new pages are
introduced to an ongoing crawl rather than starting with a fixed test
set.  We demonstrate that handling of the edge cases, where no changes
or only changes have been observed, is critical to correct estimation
over time. We also propose adaptations to the initial estimation and
search path to optimize the freshness of the corpus over a series of
crawl samples. 


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