GADT: A Probability Space ADT For Representing and Querying the Physical World

Research output: Contribution to journal › Journal article › Research › peer-review

Standard

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

Harvard

Faradjian, A, Gehrke, J & Bonnet, P 2002, 'GADT: A Probability Space ADT For Representing and Querying the Physical World', Proceeding of ICDE 2002, no. 0, pp. 201-211. https://doi.org/10.1109/ICDE.2002.994710

APA

Faradjian, A., Gehrke, J., & Bonnet, P. (2002). GADT: A Probability Space ADT For Representing and Querying the Physical World. Proceeding of ICDE 2002, (0), 201-211. https://doi.org/10.1109/ICDE.2002.994710

Vancouver

Faradjian A, Gehrke J, Bonnet P. GADT: A Probability Space ADT For Representing and Querying the Physical World. Proceeding of ICDE 2002. 2002;(0):201-211. https://doi.org/10.1109/ICDE.2002.994710

Author

Faradjian, Anton ; Gehrke, Johannes ; Bonnet, Philippe. / GADT : A Probability Space ADT For Representing and Querying the Physical World. In: Proceeding of ICDE 2002. 2002 ; No. 0. pp. 201-211.

Bibtex

@article{9dbd9bb074c511dbbee902004c4f4f50,
title = "GADT: A Probability Space ADT For Representing and Querying the Physical World",
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 (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.",
author = "Anton Faradjian and Johannes Gehrke and Philippe Bonnet",
year = "2002",
doi = "10.1109/ICDE.2002.994710",
language = "English",
pages = "201--211",
journal = "Proceeding of ICDE 2002",
number = "0",

}

RIS

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