摘要
Sensors are ubiquitous in the Internet of Things for measuring and collecting data. Analyzing these data derived from sensors is an essential task and can reveal useful latent information besides the data. Since the Internet of Things contains many sorts of sensors, the measurement data collected by these sensors are multi-type data, sometimes contai- ning temporal series information. If we separately deal with different sorts of data, we will miss useful information. This paper proposes a method to dis- cover the correlation in multi-faceted data, which contains many types of data with temporal informa- tion, and our method can simultaneously deal with multi-faceted data. We transform high-dimensional multi-faeeted data into lower-dimensional data which is set as multivariate Gaussian Graphical Models, then mine the correlation in multi-faceted data by discover the structure of the multivariate Gausslan Graphical Models. With a real data set, we verifies our method, and the experiment demonstrates that the method we propose can correctly fred out the correlation among multi-faceted meas- urement data.
Sensors are ubiquitous in the Internet of Things for measuring and collecting data.Analyzing these data derived from sensors is an essential task and can reveal useful latent information besides the data.Since the Internet of Things contains many sorts of sensors,the measurement data collected by these sensors are multi-type data,sometimes containing temporal series information.If we separately deal with different sorts of data,we will miss useful information.This paper proposes a method to discover the correlation in multi-faceted data,which contains many types of data with temporal information,and our method can simultaneously deal with multi-faceted data.We transform high-dimensional multi-faceted data into lower-dimensional data which is set as multivariate Gaussian Graphical Models,thenmine the correlation in multi-faceted data by discover the structure of the multivariate Gaussian Graphical Models.With a real data set,we verifies our method,and the experiment demonstrates that the method we propose can correctly find out the correlation among multi-faceted measurement data.
基金
the Project"The Basic Research on Internet of Things Architecture"supported by National Key Basic Research Program of China(No.2011CB302704)
supported by National Natural Science Foundation of China(No.60802034)
Specialized Research Fund for the Doctoral Program of Higher Education(No.20070013026)
Beijing Nova Program(No.2008B50)
"New generation broadband wireless mobile communication network"Key Projects for Science and Technology Development(No.2011ZX03002-002-01)
