Maritime data technology landscape and value chain exploiting oceans of data for maritime applications

José Ferreira, C. Agostinho, R. Lopes, K. Chatzikokolakis, D. Zissis, Maria-Esther Vidal, Spyros Mouzakitis
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引用次数: 6

Abstract

Maritime areas covers a large percentage of our world, being most of this area unexplored. Despite this, the sea has one of the most valuable and mostly exploited “economic platforms” of mankind, with applications in different sectors (as fishing industry, transportation cargo, etc.). Although this situation and the great evolution in technology can contribute to better know of the sea, this has not been happening. Given that a systematic collection of maritime data has already been carried out, yet is still dispersed and not used in its entirety. This is one of the objectives of the H2020 BigDataOcean project (http://www.bigdataocean.eu/site/), collecting the various data sources and thus being able to treat them together in order to obtain better results. This paper presents the analysis of the current landscape of big data, starting from the identification of existing ones, used tools and methodologies to be integrated in the project services, and platform with the aim of retrieving and analyzing the maritime data is presented. Then, the requirement engineering methodology is presented, being the methodology used during the project to identify the stakeholders, data sources, data value chain and the technologic gaps, resulting the in the identification of the first iteration of the requirements.
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为海事应用开发海洋数据的海事数据技术景观和价值链
海洋覆盖了我们世界的很大一部分,其中大部分尚未开发。尽管如此,海洋是人类最有价值和最常被利用的“经济平台”之一,在不同的领域(如渔业、运输货物等)都有应用。虽然这种情况和技术的巨大发展有助于更好地了解海洋,但这并没有发生。鉴于已经进行了系统的海事数据收集,但仍然分散且未全部使用。这是H2020 BigDataOcean项目(http://www.bigdataocean.eu/site/)的目标之一,收集各种数据源,从而能够将它们一起处理,以获得更好的结果。本文分析了大数据的现状,从识别现有数据,使用的工具和方法集成到项目服务中,并提出了旨在检索和分析海事数据的平台。然后,提出了需求工程方法,该方法在项目中用于识别涉众、数据源、数据价值链和技术差距,从而确定需求的第一次迭代。
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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