| Abstract |
|
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 (pdf's). We introduce a new object-relational data type, the Gaussian ADT GADT, that models physical data as gaussian pdf's, and we show that existing index structures can be used as fast access methods for GADT data. We also present a measure-theoretic model of probabilistic data and evaluate GADT in its light.
|
 Additional Information
|
Index Terms- Data models, probabilistic data, measurement data, sensor databases, gaussian data, probabilistic ADT's, measure theory, object-relational datatypes, access methods.
Citation:
Anton Faradjian, Johannes Gehrke, Philippe Bonnet,
"GADT: A Probability Space ADT for Representing and Querying the Physical World,"
icde,
p. 0201,
18th International Conference on Data Engineering (ICDE'02),
2002
|
