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.

• Type: Conference
• Year: 2004
• Where: DMSN
• Authors: Vibhore Kumar, Brian F. Cooper, Shamkant B. Navathe
• Keywords: Acceptable Approximation, DMSN 2004, Networked Sensors, Sensor Battery Power