NDLI: Continuous sampling from distributed streams

Content Provider	ACM Digital Library
Author	Cormode, Graham Muthukrishnan, S. Yi, Ke Zhang, Qin
Copyright Year	2012
Abstract	A fundamental problem in data management is to draw and maintain a sample of a large data set, for approximate query answering, selectivity estimation, and query planning. With large, streaming data sets, this problem becomes particularly difficult when the data is shared across multiple distributed sites. The main challenge is to ensure that a sample is drawn uniformly across the union of the data while minimizing the communication needed to run the protocol on the evolving data. At the same time, it is also necessary to make the protocol lightweight, by keeping the space and time costs low for each participant. In this article, we present communication-efficient protocols for continuously maintaining a sample (both with and without replacement) from $\textit{k}$ distributed streams. These apply to the case when we want a sample from the full streams, and to the sliding window cases of only the $\textit{W}$ most recent elements, or arrivals within the last $\textit{w}$ time units. We show that our protocols are optimal (up to logarithmic factors), not just in terms of the communication used, but also the time and space costs for each participant.
Starting Page	1
Ending Page	25
Page Count	25
File Format	PDF
ISSN	00045411
e-ISSN	1557735X
DOI	10.1145/2160158.2160163
Journal	Journal of the ACM (JACM)
Volume Number	59
Issue Number	2
Language	English
Publisher	Association for Computing Machinery (ACM)
Publisher Date	2012-05-03
Publisher Place	New York
Access Restriction	One Nation One Subscription (ONOS)
Subject Keyword	Distributed tracking Random sampling
Content Type	Text
Resource Type	Article
Subject	Hardware and Architecture Information Systems Control and Systems Engineering Artificial Intelligence Software

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