Throughout the last decade, the rise of open data in research has led to the release of many scientific datasets. Air transportation data are of particular interest given their high impact on migration, epidemic models and so on. Existing datasets are often specifically hand-curated, with the support of volunteers. One major problem with such datasets is that the quality, in terms of correctness and coverage, of the data is often not clear. Commercial datasets, on the other hand, are expensive and unnecessarily increase the entrance barrier for new researchers. In this work, a worldwide, machine-generated airfield database, which includes information on airports, helipads and runways serving airports, was derived. The database was generated using three machine-processable open datasets as input (OpenStreetMap, DBPedia and Wikipedia). Advanced data management techniques enabled the generation of an airfield database comparable with the best existing commercial, hand-curated solutions. This work contributes towards using open big data in air transportation. Furthermore, this machine-generated database, which can be automatically updated at any time, will hopefully raise the interest of other researchers in the community and lay the foundation for a gold standard for air transportation research.

中文翻译：

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一个全球性的、机器生成的机场数据库：比手工整理的数据集更好？

在过去的十年中，研究中开放数据的兴起导致了许多科学数据集的发布。鉴于航空运输数据对迁移、流行病模型等的重大影响，它们特别令人感兴趣。现有的数据集通常是在志愿者的支持下专门手工策划的。此类数据集的一个主要问题是，就正确性和覆盖范围而言，数据的质量通常不明确。另一方面，商业数据集很昂贵，而且不必要地增加了新研究人员的进入门槛。在这项工作中，导出了一个全球范围内的机器生成的机场数据库，其中包括有关机场、直升机停机坪和机场跑道的信息。该数据库是使用三个机器可处理的开放数据集作为输入（OpenStreetMap、DBPedia 和维基百科）生成的。先进的数据管理技术使机场数据库的生成能够与现有最好的商业、手工策划的解决方案相媲美。这项工作有助于在航空运输中使用开放大数据。此外，这个机器生成的数据库可以随时自动更新，有望引起社区其他研究人员的兴趣，并为航空运输研究的黄金标准奠定基础。