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DMSN
2004
ACM

Predictive filtering: a learning-based approach to data stream filtering

10 years 4 months ago
Predictive filtering: a learning-based approach to data stream filtering
Recent years have witnessed an increasing interest in filtering of distributed data streams, such as those produced by networked sensors. The focus is to conserve bandwidth and sensor battery power by limiting the number of updates sent from the source while maintaining an acceptable approximation of the value at the sink. We propose a novel technique called Predictive Filtering. We use matching predictors at the source and the sink simultaneously to predict the next update. The update is streamed only when the difference between the actual and the predicted value at the source increases beyond a threshold. Different predictors can be plugged into our framework, and we present a comparison of the effectiveness of various predictors. Through experiments performed on a bee-motion tracking log we demonstrate the effectiveness of our algorithm in limiting the number of updates while maintaining a good approximation of the streamed data at the sink.
Vibhore Kumar, Brian F. Cooper, Shamkant B. Navath
Added 30 Jun 2010
Updated 30 Jun 2010
Type Conference
Year 2004
Where DMSN
Authors Vibhore Kumar, Brian F. Cooper, Shamkant B. Navathe
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