Scalable regular pattern mining in evolving body sensor data

The recent emergence of body sensor networks (BSNs) has made it easy to continuously collect and process various health-oriented data related to temporal, spatial and vital sign monitoring of a patient. As such, discovering or mining interesting knowledge from the BSN data stream is becoming an important issue to promote and assist important decision making in healthcare. In this paper, we focus on mining the inherent regularity of different parameter readings obtained from different body sensors related to vital sign data of a patent for the purpose of following up health condition to prevent some kinds of chronic diseases. Specifically, we design and develop an efficient and scalable regular pattern mining technique that can mine the complete set of periodically/regularly occurring patterns in BSN data stream based on a user-specified periodicity/regularity threshold for the data and the subject. Various experiments in centralized and distributed environment were carried on both real and synthetic data to validate the efficiency of the proposed scalable regular pattern mining technique as compared to state-of-the-art approaches.