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Model-based views have recently been proposed as an effective method for querying noisy sensor data. Commonly used models from the AI literature (e.g., the hidden Markov model) expose to applications a stream of probabilistic and correlated state estimates computed from the sensor data. Many applications want to detect sophisticated patterns of states from these Markovian streams. Such queries are called event queries. In this paper, the authors present a new storage manager, Caldera, for processing event queries over stored Markovian streams in the Lahar system. At the heart of Caldera is a set of access methods for Markovian streams that can improve event query performance by orders of magnitude compared to existing techniques, which must scan the entire stream.
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