An ODT-based abstraction for mining closed sequential temporal patterns in IoT-cloud smart homes

Due to the large amount of usage data collected from smart home appliances in an IoT-cloud environment, efficient mining techniques are of great need to capture the behavioral patterns. Existing mining algorithms are time-consuming and error prone as the amount of data is increasing rapidly. In this paper, we propose an abstraction approach to model temporal data based on an ordered decision tree (ODT) and spatiotemporal characteristics of usage data for IoT-cloud paradigm. The contribution of this research is to provide an efficient representation in terms of average length of patterns, while preserving the spatiotemporal characteristics of original data. We performed extensive experiments on synthetic data to report the performance and provide a comparison with state-of-the-art algorithms to prove the correctness of the proposed technique, even at a low-level of abstraction. The results indicate that the proposed methodology outperform existing techniques due to the inherited power of the ODT temporal structure.