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