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

Authors Ho-Leung Chan, Tak-Wah Lam, Lap-Kei Lee, Hing-Fung Ting

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Ho-Leung Chan
Tak-Wah Lam
Lap-Kei Lee
Hing-Fung Ting

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Ho-Leung Chan, Tak-Wah Lam, Lap-Kei Lee, and Hing-Fung Ting. Continuous Monitoring of Distributed Data Streams over a Time-based Sliding Window. In 27th International Symposium on Theoretical Aspects of Computer Science. Leibniz International Proceedings in Informatics (LIPIcs), Volume 5, pp. 179-190, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2010)


The past decade has witnessed many interesting algorithms for maintaining statistics over a data stream. This paper initiates 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. This paper presents communication-efficient algorithms for three classical statistics, namely, basic counting, frequent items and quantiles. The worst-case communication cost over a window is $O(\frac{k}{\varepsilon} \log \frac{\varepsilon N}{k})$ bits for basic counting and $O(\frac{k}{\varepsilon} \log \frac{N}{k})$ 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 $\varepsilon < 1$ is the desired error bound. Matching and nearly matching lower bounds are also obtained.
  • Algorithms
  • distributed data streams
  • communication efficiency
  • frequent items


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