An Interpolation Approach for Missing Context Data Based on the Time-Space Relationship and Association Rule Mining

The miss of context data is an inevitable problem of context information processing mechanism, the interpolation technique of missing data is also a research hotspot in data mining. However, the existing interpolation technique of missing data is not suitable for the flow data form of context information that does not make full use of data relevance between every collecting sensor. Moreover, that does not take Time-Space Relationship into account. In order to conquer the shortcomings and deficiencies of the existing interpolation technique of missing data, in this paper an interpolation technique for missing context data based on Time-Space Relationship and Association Rule Mining (TSRARM) is proposed to perform spatiality and time series analysis on sensor data, which generates strong association rules to interpolate missing data. Finally, the simulation experiment verifies the rationality and efficiency of TSRARM through the acquisition of temperature sensor data. Experiments show that the algorithms are of high accuracy for the interpolation of missing context data, which are Simple Linear Regression (SLR) algorithm and the EM algorithm. In addition, it has smaller time and space overhead and can guarantee Quality of Service (QoS) of real-time applications.