Analysis, ranking and prediction in pervasive computing trails

Many pervasive computing applications involve the recording of user interaction with physical and digital resources in the environment. Such records can be used to establish context histories that can be subsequently used for user behaviour analysis, pattern recognition, prediction, and the provision of context aware services. In this paper we use trails as the principal data processing primitive for analysis and prediction. We define a trail as the sequence of recorded interactions with the pervasive computing space. Trails contain patterns of space usage and they can be used for the provision of different services, space usage analysis or sociological information of people using the environment simultaneously. Trail analysis requires considerable storage and computational resources to discover such patterns. Moreover no single method exists that identifies significant trails based on different metrics for a variety of different pervasive computing application. In this paper, we introduce a trail based analysis approach, an associated model for the representation of trails and trail aggregates, and suitable data structures for efficient storage, filtering and retrieval. Also, we propose several related algorithms and associated metrics for ranking and identifying significant trails. We use these techniques in 2 different case studies to extract valuable information about the pervasive system environment usage and evaluate the summarizability and the predictive power of our model.