A Use Case in Semantic Modelling and Ranking for the Sensor Web

author: Liliana Cabral, Knowledge Media Institute (KMI), Open University (OU)
published: Dec. 19, 2014,   recorded: October 2014,   views: 13
Categories

Slides

Related content

Report a problem or upload files

If you have found a problem with this lecture or would like to send us extra material, articles, exercises, etc., please use our ticket system to describe your request and upload the data.
Enter your e-mail into the 'Cc' field, and we will keep you updated with your request's status.
Lecture popularity: You need to login to cast your vote.
  Delicious Bibliography

Description

Agricultural decision support systems are an important application of real-time sensing and environmental monitoring. With the continuing increase in the number of sensors deployed, selecting sensors that are fit for purpose is a growing challenge. Ontologies that represent sensors and observations can form the basis for semantic sensor data infrastructures. Such ontologies may help to cope with the problems of sensor discovery, data integration, and re-use, but need to be used in conjunction with algorithms for sensor selection and ranking. This paper describes a method for selecting and ranking sensors based on the requirements of predictive models. It discusses a Viticulture use case that demonstrates the complexity of semantic modelling and reasoning for the automated ranking of sensors according to the requirements on environmental variables as input to predictive analytical models. The quality of the ranking is validated against the quality of outputs of a predictive model using diferent sensors.

Link this page

Would you like to put a link to this lecture on your homepage?
Go ahead! Copy the HTML snippet !

Write your own review or comment:

make sure you have javascript enabled or clear this field: