边缘计算对实时数据处理的影响

Brian Kelly
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引用次数: 0

摘要

目的:本研究旨在探讨边缘计算对实时数据处理的影响。研究方法:研究采用桌面研究方法。桌面研究指的是二手数据或无需实地考察即可收集的数据。案头研究基本上是从现有资源中收集数据,因此与实地研究相比,案头研究通常被认为是一种低成本技术,因为主要成本涉及执行人员的时间、电话费和目录。因此,本研究依赖于已出版的研究、报告和统计数据。这些二手数据可通过在线期刊和图书馆轻松获取。研究结果:研究结果表明,在边缘计算对实时数据处理的影响方面存在背景和方法上的差距。初步实证审查显示,边缘计算通过使计算资源更接近数据源,大大减少了延迟,提高了各行业实时数据处理的效率。它强调了该技术处理大量物联网数据的能力,通过本地化数据处理提高安全性的能力,以及通过新应用和服务推动创新和经济增长的能力。事实证明,边缘计算的去中心化方法对于可靠、稳健的数据处理至关重要,尤其是在医疗保健和金融等关键领域,最终巩固了其在数字化转型中的重要地位。对理论、实践和政策的独特贡献:创新扩散理论、基于资源的观点(RBV)和社会技术系统理论可用于未来有关实时数据处理的边缘计算研究。该研究建议扩展理论框架,以纳入边缘计算的独特方面,投资于强大的边缘基础设施,并制定标准化协议和最佳实践。研究强调需要政府激励措施和支持性监管框架来促进采用,并建议学术机构将边缘计算纳入课程。此外,该研究还呼吁持续开展研究,以应对新出现的挑战和机遇,确保边缘计算技术的不断进步和有效实施。
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The Impact of Edge Computing on Real-Time Data Processing
Purpose: The study sought to explore the impact of edge computing on real-time data processing. Methodology: The study adopted a desktop research methodology. Desk research refers to secondary data or that which can be collected without fieldwork. Desk research is basically involved in collecting data from existing resources hence it is often considered a low cost technique as compared to field research, as the main cost is involved in executive’s time, telephone charges and directories. Thus, the study relied on already published studies, reports and statistics. This secondary data was easily accessed through the online journals and library. Findings: The findings reveal that there exists a contextual and methodological gap relating to the impact of edge computing on real-time data processing. Preliminary empirical review reveled that edge computing significantly reduced latency and enhanced efficiency in real-time data processing across various industries by bringing computational resources closer to data sources. It highlighted the technology's ability to handle large volumes of IoT-generated data, improve security by localizing data processing, and drive innovation and economic growth through new applications and services. Edge computing's decentralized approach proved essential for reliable and robust data handling, particularly in critical sectors like healthcare and finance, ultimately solidifying its importance in the digital transformation landscape. Unique Contribution to Theory, Practice and Policy: The Diffusion of Innovations Theory, Resource-Based View (RBV) and Sociotechnical Systems Theory may be used to anchor future studies on edge computing on real-time data processing. The study recommended expanding theoretical frameworks to include the unique aspects of edge computing, investing in robust edge infrastructure, and developing standardized protocols and best practices. It emphasized the need for government incentives and supportive regulatory frameworks to promote adoption, and suggested that academic institutions incorporate edge computing into curricula. Additionally, the study called for ongoing research to address emerging challenges and opportunities, ensuring continuous advancement and effective implementation of edge computing technologies.
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