物联网环境下多方位传感器数据的关联性挖掘

Mining Data Correlation from Multi-Faceted Sensor Data in Internet of Things

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, then mine 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.