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 -