Design and implementation of a mobile-health call system based on scalable kNN query

Abstract

With the rapid development of social security service, people have higher demands for medical service. Therefore, it is the development direction of mobile medical construction to build humanized and personalized service concepts. Mobile Health is of great significance to the society and health. Because of the slow running and unsupported distribution, the traditional computer can't solve the current issue of big data processing in healthcare industry. Mobile medical based on mobile cloud computing environment can be a good solution to these problems. The research of k Nearest Neighbor (kNN) query in mobile cloud computing environments has become a popular topic. Supporting scalable and distributed spatial data indexes has a great impact on the efficiency of KNN queries. The existing query methods are not suitable for parallelization or lead to redundancy of content. In this paper, firstly, we introduce an inverted Voronoi based kNN query processing with MapReduce. Such a query tends to be applied in various fields including m-health treatment where medical services can be provided by hospitals, clinics and other medical institutions to meet more people's medical requirements in time. Secondly, we introduce a mobile-health call system based on kNN query. Thirdly, we build a novel efficient spatial data index: Inverted Voronoi Index, which contains both inverted index and Voronoi graph. Finally, we present the outcomes of extensive experiment that are gained by both real and simulated data sets which indicate efficiency and scalability of the proposed approach.