Abstract

Various big-data analytics tools and techniques have been developed for handling massive amounts of data in the healthcare sector. However, scheduling is a significant problem to be solved in smart healthcare applications to provide better quality healthcare services and improve the efficiency of related processes when considering large medical files. For this purpose, a new hybrid model called Gaussian Relevance Vector MapReduce-based Annealed Glowworm Optimization Scheduling (GRVM-AGS) was designed to improve the balancing of large medical data files between different physicians with higher scheduling efficiency and minimal time. First, a GRVM model was developed for the predictive analysis of input medical data. This model reduces the storage complexity of large medical data analysis by means of eliminating unwanted patient information and predicts the disease class with help of a Gaussian kernel function. Afterwards, GRVM performs AGS to schedule the efficient workloads among multiple datacenters based on the luciferin value in the smart healthcare environment with reduced scheduling time. Through computational experiments, we demonstrate that GRVM-AGS increases the scheduling efficiency and reduces the scheduling time of large medical data analysis compared to state-of-the-art approaches.

摘要

已经开发了各种大数据分析工具和技术来处理医疗保健领域的海量数据。然而，在考虑大型医疗文件时，调度是智能医疗应用中需要解决的重要问题，以提供更优质的医疗服务并提高相关流程的效率。为此，一种称为基于高斯相关性矢量 MapReduce 的退火萤火虫优化调度 (GRVM-AGS) 的新混合模型旨在以更高的调度效率和最短的时间改进不同医生之间的大型医疗数据文件的平衡。首先，开发了一个 GRVM 模型用于输入医疗数据的预测分析。该模型通过消除不需要的患者信息来降低大型医疗数据分析的存储复杂性，并借助高斯核函数预测疾病类别。之后，GRVM 执行 AGS 以根据智能医疗环境中的荧光素值在多个数据中心之间调度高效的工作负载，从而减少调度时间。通过计算实验，我们证明与最先进的方法相比，GRVM-AGS 提高了调度效率并减少了大型医学数据分析的调度时间。