Large sensor networks are being widely deployed for measurement, detection and monitoring applications. Many of these applications involve database systems to store and process data from the physical world. This data has inherent measurement uncertainties that are properly represented by continuous probability distribution functions (PDFs). We introduce a new object-relational abstract data type (ADT) - the Gaussian ADT (GADT) - that models physical data as Gaussian PDFs, and we show that existing index structures can be used as fast access methods for GADT data. We also present a measurement-theoretic model of probabilistic data and evaluate GADT in its light.