GADT: A Probability Space ADT For Representing and Querying the Physical World
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GADT : A Probability Space ADT For Representing and Querying the Physical World. / Faradjian, Anton; Gehrke, Johannes; Bonnet, Philippe.
In: Proceeding of ICDE 2002, No. 0, 2002, p. 201-211.Research output: Contribution to journal › Journal article › Research › peer-review
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TY - JOUR
T1 - GADT
T2 - A Probability Space ADT For Representing and Querying the Physical World
AU - Faradjian, Anton
AU - Gehrke, Johannes
AU - Bonnet, Philippe
PY - 2002
Y1 - 2002
N2 - 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.
AB - 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.
U2 - 10.1109/ICDE.2002.994710
DO - 10.1109/ICDE.2002.994710
M3 - Journal article
SP - 201
EP - 211
JO - Proceeding of ICDE 2002
JF - Proceeding of ICDE 2002
IS - 0
ER -
ID: 135417