Paper Info
Title
Exploring Spatio-Temporal-Theme Correlation Between Physical And Social Streaming Data For Event Detection And Pattern Interpretation From Heterogeneous Sensors
Abstract
In this paper, we introduce a new method that explores spatio-temporal-theme correlations between physical and social streaming data for event detection and pattern interpretation from heterogeneous sensors. Particularly, we employ a basic two-phase framework in pattern recognition (i.e. feature extraction and detection) with the novel improvement that concerns the use of semantic information acquired from social sensors to automatically label the low-level features extracted from physical sensors. Moreover, by symbolizing the trend component of time-series data, the proposed method has an ability to interpret event's patterns to help users get insights of how events happen. Differentiating from conventional supervised learning methods whose training data are labeled manually and in an off-line mode, the proposed method can collect labels for training data automatically and in an on-line mode. Moreover, after running for a certain time, a training stage can run parallel with the detecting stage when an event model is totally built. After that, the training stage continues learning to increase the accuracy of the event model by nonstop collecting new samples with labels from streaming data. The problem of environmental factors and particularly air pollution impacts on asthma exacerbation is considered for evaluating the proposed method. The experimental results show that the proposed method can probably detect the prevalence of asthma risks in a specific spatio-temporal context as well as help users understand how a change in the surrounding environment (e. g. weather condition and air pollution) can influence their health (e. g. asthma attack) by interpreting detected event's patterns.
Year
Venue
Keywords
2015
PROCEEDINGS 2015 IEEE INTERNATIONAL CONFERENCE ON BIG DATA
Data Mining, Spatio-Temporal-Theme Correlation, Pattern Interpretation, Event Detection, Health Care
Field
DocType
Citations 
NonStop,Data mining,Event model,Computer science,Feature extraction,Supervised learning,Correlation,Artificial intelligence,Streaming data,Semantics,Machine learning,Market research
Conference
0
PageRank 
References 
Authors
0.34
12
5
Name
Order
Citations
PageRank
Minh-son Dao19321.42
Koji Zettsu221239.07
Siripen Pongpaichet3526.46
Laleh Jalali4406.19
Ramesh Jain576301861.65