Abstract
A ‘smart space’ is one that automatically identifies and tracks its occupants using unobtrusive biometric modalities such as face, gait, and voice in an unconstrained fashion. Information retrieval in a smart space is concerned with information about the location of people at various points in time. Towards this end, we abstract a smart space by a probabilistic state transition system in which each state records the probabilities of presence of a set of individuals who are present in various zones of the smart space. We formulate a data model based upon an occupancy relation with a real-valued probability attribute and describe some of the spatio-temporal queries in SQL and CLP(R), focusing on the computation of probabilities, an aspect that is novel to this model. We define concepts of precision and recall to quantify the performance of this model based on its ability to answer various spatio-temporal queries and discuss results from our experimental prototype.
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