• 文献检索
  • 文档翻译
  • 深度研究
  • 学术资讯
  • Suppr Zotero 插件Zotero 插件
  • 邀请有礼
  • 套餐&价格
  • 历史记录
应用&插件
Suppr Zotero 插件Zotero 插件浏览器插件Mac 客户端Windows 客户端微信小程序
定价
高级版会员购买积分包购买API积分包
服务
文献检索文档翻译深度研究API 文档MCP 服务
关于我们
关于 Suppr公司介绍联系我们用户协议隐私条款
关注我们

Suppr 超能文献

核心技术专利:CN118964589B侵权必究
粤ICP备2023148730 号-1Suppr @ 2026

文献检索

告别复杂PubMed语法,用中文像聊天一样搜索,搜遍4000万医学文献。AI智能推荐,让科研检索更轻松。

立即免费搜索

文件翻译

保留排版,准确专业,支持PDF/Word/PPT等文件格式,支持 12+语言互译。

免费翻译文档

深度研究

AI帮你快速写综述,25分钟生成高质量综述,智能提取关键信息,辅助科研写作。

立即免费体验

无服务器云计算中数据驱动应用的模型驱动框架。

A model-driven framework for data-driven applications in serverless cloud computing.

机构信息

Department of Computer and Software Engineering, National University of Sciences and Technology (NUST), Islamabad, Pakistan.

Computer Engineering Department, College of Computer and Information Systems, Umm Al-Qura University, Makkah, Saudi Arabia.

出版信息

PLoS One. 2020 Aug 28;15(8):e0237317. doi: 10.1371/journal.pone.0237317. eCollection 2020.

DOI:10.1371/journal.pone.0237317
PMID:32857770
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC7454969/
Abstract

In a serverless cloud computing environment, the cloud provider dynamically manages the allocation of resources whereas the developers purely focus on their applications. The data-driven applications in serverless cloud computing mainly address the web as well as other distributed scenarios, and therefore, it is essential to offer a consistent user experience across different connection types. In order to address the issues of data-driven application in a real-time distributed environment, the use of GraphQL (Graph Query Language) is getting more and more popularity in state-of-the-art cloud computing approaches. However, the existing solutions target the low level implementation of GraphQL, for the development of a complex data-driven application, which may lead to several errors and involve a significant amount of development efforts due to various users' requirements in real-time. Therefore, it is critical to simplify the development process of data-driven applications in a serverless cloud computing environment. Consequently, this research introduces UMLPDA (Unified Modeling Language Profile for Data-driven Applications), which adopts the concepts of UML-based Model-driven Architectures to model the frontend as well as the backend requirements for data-driven applications developed at a higher abstraction level. Particularly, a modeling approach is proposed to resolve the development complexities such as data communication and synchronization. Subsequently, a complete open source transformation engine is developed using a Model-to-Text approach to automatically generate the frontend as well as backend low level implementations of Angular2 and GraphQL respectively. The validation of proposed work is performed with three different case studies, deployed on Amazon Web Services platform. The results show that the proposed framework enables to develop the data-driven applications with simplicity.

摘要

在无服务器云计算环境中,云服务提供商动态管理资源分配,而开发人员则专注于他们的应用程序。无服务器云计算中的数据驱动型应用主要针对 Web 以及其他分布式场景,因此,在不同的连接类型之间提供一致的用户体验至关重要。为了解决实时分布式环境中的数据驱动型应用程序问题,GraphQL(Graph Query Language)在最先进的云计算方法中越来越受欢迎。然而,现有的解决方案针对的是 GraphQL 的低级实现,对于开发复杂的数据驱动型应用程序,由于实时的各种用户需求,可能会导致出现多个错误并涉及大量的开发工作。因此,简化无服务器云计算环境中数据驱动型应用程序的开发过程至关重要。因此,本研究引入了 UMLPDA(用于数据驱动型应用程序的统一建模语言配置文件),它采用基于 UML 的模型驱动架构的概念,以更高的抽象级别对数据驱动型应用程序的前端和后端需求进行建模。特别是,提出了一种建模方法来解决数据通信和同步等开发复杂性问题。随后,使用模型到文本的方法开发了一个完整的开源转换引擎,分别自动生成 Angular2 和 GraphQL 的前端和后端低级实现。通过在 Amazon Web Services 平台上部署三个不同的案例研究来验证所提出的工作,结果表明,所提出的框架可以简化数据驱动型应用程序的开发。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/64c2/7454969/5b8bc908ec52/pone.0237317.g020.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/64c2/7454969/97f672cf48c6/pone.0237317.g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/64c2/7454969/cc1ca18b9c1e/pone.0237317.g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/64c2/7454969/b547f279c021/pone.0237317.g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/64c2/7454969/90682ecffdd7/pone.0237317.g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/64c2/7454969/76cd3ec15f93/pone.0237317.g005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/64c2/7454969/f4db404a90f1/pone.0237317.g006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/64c2/7454969/02db1d6411bc/pone.0237317.g007.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/64c2/7454969/ff1f183ec325/pone.0237317.g008.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/64c2/7454969/cabf6b4755a0/pone.0237317.g009.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/64c2/7454969/2c84ed403143/pone.0237317.g010.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/64c2/7454969/0102b2551564/pone.0237317.g011.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/64c2/7454969/ff82d650d895/pone.0237317.g012.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/64c2/7454969/ca8cf0cab4b1/pone.0237317.g013.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/64c2/7454969/23b38f5c8b86/pone.0237317.g014.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/64c2/7454969/7ff5b83dcb8c/pone.0237317.g015.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/64c2/7454969/50cb4c346564/pone.0237317.g016.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/64c2/7454969/5f07f02f5b0b/pone.0237317.g017.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/64c2/7454969/6bf5de4cfc58/pone.0237317.g018.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/64c2/7454969/21e74ff893e8/pone.0237317.g019.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/64c2/7454969/5b8bc908ec52/pone.0237317.g020.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/64c2/7454969/97f672cf48c6/pone.0237317.g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/64c2/7454969/cc1ca18b9c1e/pone.0237317.g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/64c2/7454969/b547f279c021/pone.0237317.g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/64c2/7454969/90682ecffdd7/pone.0237317.g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/64c2/7454969/76cd3ec15f93/pone.0237317.g005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/64c2/7454969/f4db404a90f1/pone.0237317.g006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/64c2/7454969/02db1d6411bc/pone.0237317.g007.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/64c2/7454969/ff1f183ec325/pone.0237317.g008.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/64c2/7454969/cabf6b4755a0/pone.0237317.g009.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/64c2/7454969/2c84ed403143/pone.0237317.g010.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/64c2/7454969/0102b2551564/pone.0237317.g011.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/64c2/7454969/ff82d650d895/pone.0237317.g012.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/64c2/7454969/ca8cf0cab4b1/pone.0237317.g013.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/64c2/7454969/23b38f5c8b86/pone.0237317.g014.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/64c2/7454969/7ff5b83dcb8c/pone.0237317.g015.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/64c2/7454969/50cb4c346564/pone.0237317.g016.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/64c2/7454969/5f07f02f5b0b/pone.0237317.g017.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/64c2/7454969/6bf5de4cfc58/pone.0237317.g018.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/64c2/7454969/21e74ff893e8/pone.0237317.g019.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/64c2/7454969/5b8bc908ec52/pone.0237317.g020.jpg

相似文献

1
A model-driven framework for data-driven applications in serverless cloud computing.无服务器云计算中数据驱动应用的模型驱动框架。
PLoS One. 2020 Aug 28;15(8):e0237317. doi: 10.1371/journal.pone.0237317. eCollection 2020.
2
Cloud-based serverless computing enables accelerated monte carlo simulations for nuclear medicine imaging.基于云的无服务器计算可实现核医学成像的加速蒙特卡罗模拟。
Biomed Phys Eng Express. 2024 Jun 25;10(4). doi: 10.1088/2057-1976/ad5847.
3
Serverless computing in omics data analysis and integration.无服务器计算在组学数据分析和整合中的应用。
Brief Bioinform. 2022 Jan 17;23(1). doi: 10.1093/bib/bbab349.
4
Serverless Workflows for Containerised Applications in the Cloud Continuum.云连续体中容器化应用的无服务器工作流。
J Grid Comput. 2021;19(3):30. doi: 10.1007/s10723-021-09570-2. Epub 2021 Jul 13.
5
Generation of a dataset for DoW attack detection in serverless architectures.用于无服务器架构中检测拒绝服务攻击的数据集生成
Data Brief. 2023 Dec 5;52:109921. doi: 10.1016/j.dib.2023.109921. eCollection 2024 Feb.
6
HealtheDataLab - a cloud computing solution for data science and advanced analytics in healthcare with application to predicting multi-center pediatric readmissions.HealtheDataLab- 一个针对医疗保健领域的数据科学和高级分析的云计算解决方案,应用于预测多中心儿科再入院率。
BMC Med Inform Decis Mak. 2020 Jun 19;20(1):115. doi: 10.1186/s12911-020-01153-7.
7
Development and evaluation of SOA-based AAL services in real-life environments: a case study and lessons learned.基于 SOA 的 AAL 服务在现实环境中的开发和评估:案例研究与经验教训。
Int J Med Inform. 2013 Nov;82(11):e269-93. doi: 10.1016/j.ijmedinf.2011.03.007. Epub 2011 Apr 9.
8
Cost-aware orchestration of applications over heterogeneous clouds.基于成本感知的跨异构云应用编排。
PLoS One. 2020 Feb 18;15(2):e0228086. doi: 10.1371/journal.pone.0228086. eCollection 2020.
9
FireFace: Leveraging Internal Function Features for Configuration of Functions on Serverless Edge Platforms.FireFace:利用内部功能特性在无服务器边缘平台上配置功能
Sensors (Basel). 2023 Sep 12;23(18):7829. doi: 10.3390/s23187829.
10
MCX Cloud-a modern, scalable, high-performance and in-browser Monte Carlo simulation platform with cloud computing.MCX 云——一个现代化、可扩展、高性能的网页端蒙特卡罗模拟平台,具有云计算能力。
J Biomed Opt. 2022 Jan;27(8). doi: 10.1117/1.JBO.27.8.083008.

引用本文的文献

1
Medicare meets the cloud: the development of a secure platform for the storage and analysis of claims data.医疗保险与云计算相遇:一个用于存储和分析理赔数据的安全平台的开发。
JAMIA Open. 2024 Feb 9;7(1):ooae007. doi: 10.1093/jamiaopen/ooae007. eCollection 2024 Apr.