We present a framework called Semantic Streams that allows users to pose declarative queries over semantic interpretations of sensor data. For example, instead of querying raw magnetometer data, the user queries whether vehicles are cars or trucks; the system decides which sensor data and which operations to use to infer the type of vehicle. The user can also place constraints on values such as the the amount of energy consumed or the confidence with which the vehicles are classified. We demonstrate how this system can be used on a network of video, magnetometer, and infrared break beam sensors deployed in a parking garage with three simultaneous and independent users.
%0 Book Section
%1 WhitehouseZhaoLiu06EWSN
%A Whitehouse, Kamin
%A Zhao, Feng
%A Liu, Jie
%B Wireless Sensor Networks: Third European Workshop, EWSN 2006, Zurich, Switzerland
%D 2006
%K v1205 springer paper embedded ai mobile sensor network semantic data knowledge processing web service zzz.spm
%P 5-20
%R 10.1007/11669463
%T Semantic Streams: A Framework for Composable Semantic Interpretation of Sensor Data
%X We present a framework called Semantic Streams that allows users to pose declarative queries over semantic interpretations of sensor data. For example, instead of querying raw magnetometer data, the user queries whether vehicles are cars or trucks; the system decides which sensor data and which operations to use to infer the type of vehicle. The user can also place constraints on values such as the the amount of energy consumed or the confidence with which the vehicles are classified. We demonstrate how this system can be used on a network of video, magnetometer, and infrared break beam sensors deployed in a parking garage with three simultaneous and independent users.
@incollection{WhitehouseZhaoLiu06EWSN,
abstract = {We present a framework called Semantic Streams that allows users to pose declarative queries over semantic interpretations of sensor data. For example, instead of querying raw magnetometer data, the user queries whether vehicles are cars or trucks; the system decides which sensor data and which operations to use to infer the type of vehicle. The user can also place constraints on values such as the the amount of energy consumed or the confidence with which the vehicles are classified. We demonstrate how this system can be used on a network of video, magnetometer, and infrared break beam sensors deployed in a parking garage with three simultaneous and independent users.},
added-at = {2012-05-30T10:55:58.000+0200},
author = {Whitehouse, Kamin and Zhao, Feng and Liu, Jie},
biburl = {https://www.bibsonomy.org/bibtex/2124341096f6fdcafd7887f6376102ff2/flint63},
booktitle = {Wireless Sensor Networks: Third European Workshop, EWSN 2006, Zurich, Switzerland},
crossref = {EWSN2006},
doi = {10.1007/11669463},
file = {SpringerLink:2006/WhitehouseZhaoLiu06EWSN.pdf:PDF},
groups = {public},
interhash = {fda774a69852b337a22c4830f3ffe537},
intrahash = {124341096f6fdcafd7887f6376102ff2},
keywords = {v1205 springer paper embedded ai mobile sensor network semantic data knowledge processing web service zzz.spm},
pages = {5-20},
timestamp = {2018-04-16T11:50:27.000+0200},
title = {Semantic Streams: A Framework for Composable Semantic Interpretation of Sensor Data},
username = {flint63},
year = 2006
}