A Cloud Framework Design for A Disease Symptom Self-inspection Service

A Cloud Framework Design for A Disease Symptom Self-inspection Service

Lu Yan, Ding Xiong
Copyright: © 2020 |Pages: 18
DOI: 10.4018/IRMJ.2020040101
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The establishment of a symptom self-inspection service faces will face many challenges related to how to best acquire, store, and analyze the available data. In view of the problems, a cloud framework symptom self-inspection service model is proposed in this article. A Hadoop cluster is set up to store massive medical data and provide indexing so as to produce acceptable electronic medical record search response times. A cluster of the distributed search nodes based on Lucene can be used for real-time retrieval, data analysis, and privacy filtering from a massive collection of electronic medical records. The implementation of symptom check-up services is discussed, including the selection of search nodes, the establishment of medical records index files, the ranking and sorting of medical records similarity. Experimental results demonstrate that our proposed cloud framework model serves as a scalable and effective self-inspection health symptom service.
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1. Introduction

With the increasing level of computerization in medical and health care industry, as well as the continuous development and improvement of medical and health service system, people's requirements for medical and health information are getting higher and higher. At the same time, there are more and more “sub-healthy” people in the current society. According to the World Health Organization, “sub-healthy” people make up 75% of the world's population (He, Fan, & Li, 2013). In China, the number of “sub-healthy” people reached 900 million (Ding, He, & Wang, 2009). On the other hand, the generated medical data are regularly uploaded to the regional health information platform for all levels of medical institutions (Rashidi & Cook, 2009). According to the storage, analysis and retrieval of massive medical data, an effective data foundation is provided for the self-inspection of symptoms. Both sub-healthy population and patients hope to carry out the necessary self-diagnosis by means of information technology, and the relevant treatment plan is obtained for similar patients, the understanding of the main body-to-disease being further deepened (Cook, Youngblood, Heierman et al., 2003; Doctor, Hagras, & Callaghan, 2005).

Cloud computing technology is widely used in the field of big data processing because of its scalability and service-oriented features (Canny & Zhao, 2013; Cheng, Qin, & Rusu, 2012). As a leader in cloud computing technology, Hadoop is one of the relatively mature cloud computing platforms in recent years (Hu, Yu et al., 2017; Qi, Zhou et al., 2017). Due to the high fault tolerance, high performance and low cost, Hadoop has drawn the attention of academics (Rodger, 2016), it is widely used in mass data processing. Hadoop includes a distributed file system, with which big data can be handled at low cost. Hadoop can run on common computer hardware devices, and a linear increase can be achieved in storage capacity through the simple addition of data nodes, so the data storage costs are reduced drastically (Wang, Lin et al., 2017). The advent of Hadoop provides new and efficient ways to store, query, analyze and mine for massive and heterogeneous medical data.

Cloud computing is a new computing model, by which computing tasks are distributed across resource pools of large numbers of computers, and users are enabled to access computing power, storage space, and information services on demand (Zhang & Zhou, 2009). Cloud Computing Open Architecture Overview Diagram is Figure 1. The computing and storage capabilities of the cloud are utilized to improve the response efficiency of the WMS which is built on the cloud platform. As a result, server pressure is reduced, the service quality is improved.

Figure 1.

Cloud computing open architecture overview diagram


The center of the WMS cloud framework is the server, which consists of the main server Main Server, the task control server JobTracker, the data node DataNode and the distributed file system DFS, shown in Figure 2 (Chen, Wang, Tang et al., 2012). The Main Server is the receiving center and image publishing center of the WMS command; the JobTracker is the task scheduling server, responsible for receiving the GetMap command of the main server, completing the file generation process, and sending the result back to the Main Server; the DataNode is the data storage of the cloud distributed file system. The required spatial data of the WMS service is divided into multiple backups and stored on different DataNodes. In order to realize the migration of computing to storage, DataNode is also the task center for spatial image generation of each layer. DFS is Distributed File System. In the file generation process, JobTracker needs to obtain a list of data nodes for spatial data storage. This information is stored in metadata in the Distributed File System (DFS) of the Main Server. The cloud pattern file generation process is transferred to JobTracker and DataNode in Figure 2. The main server is under pressure, and there are higher response performance and throughput.

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