5,491 research outputs found
Evolving Spatially Aggregated Features from Satellite Imagery for Regional Modeling
Satellite imagery and remote sensing provide explanatory variables at
relatively high resolutions for modeling geospatial phenomena, yet regional
summaries are often desirable for analysis and actionable insight. In this
paper, we propose a novel method of inducing spatial aggregations as a
component of the machine learning process, yielding regional model features
whose construction is driven by model prediction performance rather than prior
assumptions. Our results demonstrate that Genetic Programming is particularly
well suited to this type of feature construction because it can automatically
synthesize appropriate aggregations, as well as better incorporate them into
predictive models compared to other regression methods we tested. In our
experiments we consider a specific problem instance and real-world dataset
relevant to predicting snow properties in high-mountain Asia
A Hybrid Approach for Data Analytics for Internet of Things
The vision of the Internet of Things is to allow currently unconnected
physical objects to be connected to the internet. There will be an extremely
large number of internet connected devices that will be much more than the
number of human being in the world all producing data. These data will be
collected and delivered to the cloud for processing, especially with a view of
finding meaningful information to then take action. However, ideally the data
needs to be analysed locally to increase privacy, give quick responses to
people and to reduce use of network and storage resources. To tackle these
problems, distributed data analytics can be proposed to collect and analyse the
data either in the edge or fog devices. In this paper, we explore a hybrid
approach which means that both innetwork level and cloud level processing
should work together to build effective IoT data analytics in order to overcome
their respective weaknesses and use their specific strengths. Specifically, we
collected raw data locally and extracted features by applying data fusion
techniques on the data on resource constrained devices to reduce the data and
then send the extracted features to the cloud for processing. We evaluated the
accuracy and data consumption over network and thus show that it is feasible to
increase privacy and maintain accuracy while reducing data communication
demands.Comment: Accepted to be published in the Proceedings of the 7th ACM
International Conference on the Internet of Things (IoT 2017
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