Mobile Big Data Fault-Tolerant Processing for eHealth Networks
Mobile Big Data Fault-Tolerant Processing for eHealth Networks
复制标题
电子医疗网络的移动大数据容错处理
DOI：
10.1109/mnet.2016.7389829
复制
发表时间：
2016
期刊：
影响因子：
9.3
通讯作者：
Zhang Yan
中科院分区：
文献类型：
--
作者：
Wang Kun;Shao Yun;Shu Lei;Zhu Chunsheng;Zhang Yan
在日常生活中，人们倾向于使用移动网络来获取更准确的整体数据。有了智能移动设备，几乎可以自动收集各种数据，这直接促成了电子健康的蓬勃发展。然而，大量的数据也将我们带入了大数据时代，这需要新的数据采集、传输和处理技术。为了确保无处不在的数据收集，移动电子健康网络的规模必须扩大。此外，网络将面临传输大量电子健康数据的更大压力。此外，由于处理时间随着数据量的增加而增加，即使是强大的处理器对于大数据来说也不一定是高效的。为了解决这些问题，本文提出了一种基于兴趣的约简变量邻域搜索（RVNS）队列架构（IRQA）。在该三层体系结构中，设计了基于兴趣匹配的容错机制，保证了数据采集层电子健康数据的完整性。然后数据整合层对数据的准确性进行检验，并为数据处理做准备。最后，在数据分析层采用RVNS队列进行快速数据处理。在按照相关规则进行处理后，只有有价值的数据才会报告给医疗保健提供者，从而节省了他们识别这些数据的工作量。仿真结果表明，IRQA能够稳定、快速地处理大量数据。
In daily life, people tend to use mobile networks for more accurate overall data. With intelligent mobile devices, almost all kinds of data can be collected automatically, which contributes directly to the blooming of eHealth. However, large amounts of data are also leading us into the era of big data, in which new data collection, transmission, and processing techniques are required. To ensure ubiquitous data collection, the scale of mobile eHealth networks has to be expanded. Also, networks will face more pressure to transmit large amounts of eHealth data. In addition, because the processing time increases with data volume, even powerful processors cannot always be regarded as efficient for big data. To solve these problems, in this article, an interests-based reduced variable neighborhood search (RVNS) queue architecture (IRQA) is proposed. In this three-layer architecture, a fault-tolerant mechanism based on interests matching is designed to ensure the completeness of eHealth data in the data gathering layer. Then the data integrating layer checks the accuracy of data, and also prepares for data processing. In the end, an RVNS queue is adopted for rapid data processing in the data analyzing layer. After processing with relevant rules, only valuable data will be reported to health care providers, which saves their effort to identify these data. Simulation shows that IRQA is steady and fast enough to process large amounts of data.