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Erschienen in:
01.09.2016
Irrevocable-choice algorithms for sampling from a stream
Erschienen in: Data Mining and Knowledge Discovery | Ausgabe 5/2016
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Abstract
The problem of sampling from data streams has attracted significant interest in the last decade. Whichever sampling criteria is considered (uniform sample, maximally diverse sample, etc.), the challenges stem from the relatively small amount of memory available in the face of unbounded streams. In this work we consider an interesting extension of this problem, the framework of which is stimulated by recent improvements in sensing technologies and robotics. In some situations it is not only possible to digitally sense some aspects of the world, but to physically capture a tangible aspect of that world. Currently deployed examples include devices that can capture water/air samples, and devices that capture individual insects or fish. Such devices create an interesting twist on the stream sampling problem, because in most cases, the decision to take a physical sample is irrevocable. In this work we show how to generalize diversification sampling strategies to the irrevocable-choice setting, demonstrating our ideas on several real world domains.