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What and Who

Continuous Monitoring of Distributed Data Streams over a Time-based Sliding Window

Lap-Kei Lee
Max-Planck-Institut für Informatik - D1
AG1 Mittagsseminar (own work)
AG 1, AG 4, RG1, MMCI, AG 3, AG 5, SWS  
AG Audience

Date, Time and Location

Monday, 8 February 2010
30 Minutes
E1 4


The past decade has witnessed many interesting algorithms for maintaining statistics over a data stream.

We initiate a theoretical study of algorithms for monitoring distributed data streams over a time-based
sliding window (which contains a variable number of items and possibly out-of-order items). The concern
is how to minimize the communication between individual streams and the root, while allowing the root,
at any time, to be able to report the global statistics of all streams within a given error bound. We give
communication-efficient algorithms for three classical statistics, namely, basic counting, frequent items
and quantiles. The worst-case communication cost over a window is O((k/epsilon) log(epsilon N/k)) bits for
basic counting and O((k/epsilon) log(N/k)) words for the remainings, where k is the number of distributed
data streams, N is the total number of items in the streams that arrive or expire in the window, and
epsilon < 1 is the desired error bound. Matching and nearly matching lower bounds are also obtained.


Lap-Kei Lee
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Lap-Kei Lee, 02/04/2010 16:08 -- Created document.