面向边缘-云连续体的高效RDF流处理
Efficient RDF Streaming for the Edge-Cloud Continuum
AI总结：
针对边缘-云连续体场景中语义数据流处理缺乏合适方案的问题，本文提出端到端RDF流处理方法Jelly，实现简单且弹性高，实验证明其吞吐量和延迟性能远超现有方法。
摘要：
随着大规模分布式系统逐步向边缘-云连续体转变，对通用、可扩展、实用且基于成熟技术的软件解决方案的需求日益迫切。与此同时，语义技术，尤其是在流处理场景下，对于实现边缘-云系统中的互操作性正变得越来越重要。然而，近年来语义数据流领域一直停滞不前，目前尚无能够满足上述需求的可用解决方案。为填补这一空白，本文提出了一种新颖的端到端RDF流处理方法（命名为Jelly）。该方法实现简单，同时具有很高的弹性，旨在适配广泛多样的使用场景。通过一系列实验对其实际性能进行了评估，包括端到端吞吐量和延迟测量。结果表明，Jelly的性能远超当前可用的方法。所提出的方法在推动各类应用中实现高性能语义数据处理方面取得了重大进展，包括未来的边缘-云系统。此外，本研究为在实际场景中应用和评估该方法开辟了可能性，这将作为后续研究的重点。
英文摘要：
With the ongoing, gradual shift of large-scale distributed systems towards the edge-cloud continuum, the need arises for software solutions that are universal, scalable, practical, and grounded in well-established technologies. Simultaneously, semantic technologies, especially in the streaming context, are becoming increasingly important for enabling interoperability in edge-cloud systems. However, in recent years, the field of semantic data streaming has been stagnant, and there are no available solutions that would fit those requirements. To fill this gap, in this contribution, a novel end-to-end RDF streaming approach is proposed (named Jelly). The method is simple to implement, yet very elastic, and designed to fit a wide variety of use cases. Its practical performance is evaluated in a series of experiments, including end-to-end throughput and latency measurements. It is shown that Jelly achieves vastly superior performance to the currently available approaches. The presented method makes significant progress towards enabling high-performance semantic data processing in a wide variety of applications, including future edge-cloud systems. Moreover, this study opens up the possibility of applying and evaluating the method in real-life scenarios, which will be the focus of further research.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
