LSPEnv: Location-based service provider for environmental data
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LSPEnv : Location-based service provider for environmental data. / Wac, Katarzyna; Ragia, Lemonia.
In: Journal of Location Based Services, Vol. 2, No. 4, 29.12.2008, p. 287-302.Research output: Contribution to journal › Journal article › Research › peer-review
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TY - JOUR
T1 - LSPEnv
T2 - Location-based service provider for environmental data
AU - Wac, Katarzyna
AU - Ragia, Lemonia
PY - 2008/12/29
Y1 - 2008/12/29
N2 - The state of our environment becomes a very important issue and especially people with health problems need more information and support in their daily life. This article presents an approach for forecasting values of several environmental-state variables as a basis for location-based services. We propose a system for making predictions for several spatial temporal variables using the Bayesian Network method as a machine learning technique. The system is based on a three-tier architecture, which assists the environmental data acquisition, processing and dissemination of predictions. To handle the missing values of data we use the structural expectation maximisation algorithm. The system's evaluation case study is based on real environmental data acquired from the Swiss national network. The data represents several environmental-state variables at different types of location, e.g. rural, urban, and at different times in a time span of a year.
AB - The state of our environment becomes a very important issue and especially people with health problems need more information and support in their daily life. This article presents an approach for forecasting values of several environmental-state variables as a basis for location-based services. We propose a system for making predictions for several spatial temporal variables using the Bayesian Network method as a machine learning technique. The system is based on a three-tier architecture, which assists the environmental data acquisition, processing and dissemination of predictions. To handle the missing values of data we use the structural expectation maximisation algorithm. The system's evaluation case study is based on real environmental data acquired from the Swiss national network. The data represents several environmental-state variables at different types of location, e.g. rural, urban, and at different times in a time span of a year.
KW - Environmental data
KW - Location-based services
KW - Machine learning
KW - Prediction
UR - http://www.scopus.com/inward/record.url?scp=57849117862&partnerID=8YFLogxK
U2 - 10.1080/17489720802612710
DO - 10.1080/17489720802612710
M3 - Journal article
AN - SCOPUS:57849117862
VL - 2
SP - 287
EP - 302
JO - Journal of Location Based Services
JF - Journal of Location Based Services
SN - 1748-9725
IS - 4
ER -
ID: 225419974