Paper Info
Title
Discovering Environmental Impacts on Public Health Using Heterogeneous Big Sensory Data
Abstract
In this paper, we present a method for detecting events, especially healthcare-related events, by abstracting trends of data streaming from heterogeneous sensors. The main idea behind the method is to detect real-time events and explain them understandably by finding spatio-temporal-theme correlations between physical and social sensory data. In the method, a training stage is designed as a non-stop process with labels assigned automatically to feature vectors in order to build a set of positive and negative samples. Thereafter, an event model is generated by using supervised learning approaches as a means to steadily increase its accuracy. The problem of environmental impacts on asthma attacks is used to evaluate the proposed method. Experimental results show that the proposed method can detect the prevalence of asthma risks in a specific spatio-temporal context with high accuracy.
Year
DOI
Venue
2015
10.1109/BigDataCongress.2015.122
BigData Congress
Keywords
Field
DocType
Heterogeneous Sensory Data, Event Detection, Social Networks, Asthma Attacks, Environmental Impact, Public Health
Public health,Data mining,Feature vector,Social network,Event model,Computer science,Supervised learning,Feature extraction,Correlation,Sensory system
Conference
ISSN
Citations 
PageRank 
2379-7703
0
0.34
References 
Authors
6
2
Name
Order
Citations
PageRank
Minh-son Dao19321.42
Koji Zettsu221239.07