Sequential-index pattern mining for lifecare telecommunication platform

Abstract

Lifecare for quality-of-life improvement includes not only the prevention and treatment of diseases and health management anywhere and at any-time, but also the improvement of lifestyle, the psychological environment, and the emergency response. The construction of a sustainable model on the telecommunications platform as a solution has been constantly studied using intelligence information technology. In this study, sequential-index pattern mining on the lifecare telecommunications platform is proposed. The method is as follows. Various temporal and regional indices that are provided by the Meteorological Administration, Meteorological Data Open Portal, Public Data Portal, Healthcare Big Data Hub, and Korea Environment Corporation are collected as lifecare index data. The collected index data are transmitted to the NAS file server of the lifecare telecommunications platform using peer-to-peer (P2P)-based interconnection technology and high-performance computing. The sequential relationships among the indices are discovered to consider the semantic relationships. Using the AprioriAll mining algorithm, the maximum sequence is determined to find sequential relationships in the frequent sequence set. To process the pattern extraction and computation efficiently, recomposed transactions are stored in the high-performance NAS file server. Also, a mining-index prediction model is developed in consideration of the sequential relationships to complement the weakness of the statistical time-series analysis. Since the regional meteorological conditions are similar, the index sequential pattern can solve the problem of the generation of omissions or errors regarding the real-time monitoring data that are due to the monitoring equipment or communication failures. Therefore, the proposed sequential-index pattern mining can be utilized as a lifecare tool for risk-factor detection, risk prediction, and trend analysis.