Big Data Pipelines on the Computing Continuum: Ecosystem and Use Cases Overview

D. Roman, Nikolay Nikolov, A. Soylu, B. Elvesæter, Hui Song, R.-C. Prodan, Dragi Kimovski, Andrea Marrella, F. Leotta, M. Matskin, Giannis Ledakis, K. Theodosiou, Anthony Simonet-Boulogne, F. Perales, E. Kharlamov, Alexandre Ulisses, Arnor Solberg, Raffaele Ceccarelli
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引用次数: 6

Abstract

Organisations possess and continuously generate huge amounts of static and stream data, especially with the proliferation of Internet of Things technologies. Collected but unused data, i.e., Dark Data, mean loss in value creation potential. In this respect, the concept of Computing Continuum extends the traditional more centralised Cloud Computing paradigm with Fog and Edge Computing in order to ensure low latency pre-processing and filtering close to the data sources. However, there are still major challenges to be addressed, in particular related to management of various phases of Big Data processing on the Computing Continuum. In this paper, we set forth an ecosystem for Big Data pipelines in the Computing Continuum and introduce five relevant real-life example use cases in the context of the proposed ecosystem.
计算连续体上的大数据管道:生态系统和用例概述
组织拥有并不断产生大量的静态和流数据,特别是随着物联网技术的普及。已收集但未使用的数据,即暗数据,意味着价值创造潜力的丧失。在这方面,计算连续体的概念通过雾和边缘计算扩展了传统的更集中的云计算范式,以确保低延迟的预处理和靠近数据源的过滤。然而,仍有一些重大挑战需要解决,特别是在计算连续体的大数据处理的各个阶段的管理。在本文中,我们为计算连续体中的大数据管道提出了一个生态系统,并在该生态系统的背景下介绍了五个相关的现实生活示例用例。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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