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Abstract Details

Activity Number: 486
Type: Invited
Date/Time: Wednesday, August 1, 2012 : 10:30 AM to 12:20 PM
Sponsor: Section on Statistical Computing
Abstract - #303691
Title: Estimating Traffic for Millions of Queries in Real Time
Author(s): Tim Hesterberg*+ and Oliver Dain and Yogita Mehta and Laramie Leavitt
Companies: Google and MIT Lincoln Laboratory and Google and Google
Address: 2628 31st Ave W, Seattle, WA, 98199, United States
Keywords: Online learning ; Poisson regression ; updating ; web traffic ; spike detection ; online algorithm

Google estimates traffic for millions of queries continuously. This is used for many applications, such as caching results for frequent queries, and looking for spikes. We describe how Google efficiently processes a wide variety of queries with frequencies ranging from tens to millions of hits per day, with widely varying activity across time of day and week, and updates these models continuously as new data is observed.

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