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Building applications on top of sensor data streams is challenging because sensor data is noisy. A model-based view can reduce noise by transforming raw sensor streams into streams of probabilistic state estimates, which smooth out errors and gaps. The authors propose a novel model-based view, the Markovian stream, to represent correlated probabilistic sequences. Applications interested in evaluating event queries-extracting sophisticated state sequences-can improve robustness by querying a Markovian stream view instead of querying raw data directly. The primary challenge is to properly handle the Markovian stream's correlations.