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MIST: Distributed Indexing and Querying in Sensor Networks using Statistical Models

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Executive Summary

The modeling of high level semantic events from low level sensor signals is important in order to understand distributed phenomena. For such content-modeling purposes, transformation of numeric data into symbols and the modeling of resulting symbolic sequences can be achieved using statistical models - Markov Chains (MCs) and Hidden Markov Models (HMMs). The authors consider the problem of distributed indexing and semantic querying over such sensor models. They present a much more efficient alternative - a distributed index structure, MIST (Model-based Index STructure), and accompanying algorithms for answering the above queries.

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