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

机译:海上数据技术的前景和价值链,利用数据海洋来进行海上应用

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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.
机译:海洋区域覆盖了我们世界的很大一部分,因为该区域的大部分尚未开发。尽管如此,海洋还是人类最有价值,最被开发的“经济平台”之一,在不同领域(如渔业,运输货物等)都有应用。尽管这种情况和技术的飞速发展可以促进人们更好地了解海洋,但这并未发生。鉴于已经对海事数据进行了系统的收集,但仍是分散的,并未完全使用。这是H2020 BigDataOcean项目(http://www.bigdataocean.eu/site/)的目标之一,它收集了各种数据源,因此能够将它们一起处理以获得更好的结果。本文从对现有大数据的识别,将要使用的工具和方法论集成到项目服务中的角度出发,对当前大数据的现状进行了分析,并提出了一个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,该方法是项目期间用于识别涉众,数据源,数据价值链和技术差距的方法,从而可以确定需求的第一次迭代。

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