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 find the useful latent information besides the data itself. Because the Internet of Things contains sorts of sensors, the measurement data collected by these sensors are multi-type data, sometimes containing temporal series information. If just separately dealing with different sorts of data, we will miss useful information. This paper proposes a method to learn the correlation among multi-faceted data, which contain 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 are set as multivariate Gaussian Graphical Models, then mine the correlation among multi-faceted data by learning the structure of the multivariate Gaussian Graphical Models. With a real data set, we verified our method, and the experiment demonstrated that the method we propose can correctly find the correlation among multi-faceted measurement data.

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