Abstract
The current spread out in Big Data analytics and the medical Internet of Things (IoT) originated the recognition of smart health. Smart health is the integration of devices, sensors, cameras, and objects or things embedded with sensors that generate an enormous amount of data known as big data. IoT-enabled smart health management assimilates the digital traces generated by things and humans including teledentistry. The key purpose of teledentistry is to analyze the data and produce valuable insights and hidden patterns to offer services and overcome the existing challenges. This article proposes a data management design using Big Data analytics for smart teledentistry planning. The proposed scheme is the customization of the present parallel platforms to accomplish effective processing. The design offers three different modules which are the data pre-processing, data ingestion, and data processing module. The pre-processing is performed to speed up the real data processing. The recent schemes lack well-organized and effective parallel data loading and proficient communication. The Big Data storage and ingestion are achieved using optimized utility and parallel mechanisms. Optimized RDD-enabled (resilient distribution) Yet Another Resource Negotiator (YARN) based proposal is designed for resourceful cluster administration and computation of medical Big Data. The proposed data design is experimentally simulated with authentic datasets using Apache Spark 3.0 framework. The proposed architecture is compared with existing state-of-the-art proposals. The simulation results reveal the efficacy of the proposed system.