Departamento de Inteligencia Artificial, Universidad Nacional de Educación a Distancia (UNED), C/Juan del Rosal, 16 Ciudad Universitaria, Madrid 28040, Spain.
Database (Oxford). 2021 May 18;2021. doi: 10.1093/database/baab026.
Over the past couple of decades, the explosion of densely interconnected data has stimulated the research, development and adoption of graph database technologies. From early graph models to more recent native graph databases, the landscape of implementations has evolved to cover enterprise-ready requirements. Because of the interconnected nature of its data, the biomedical domain has been one of the early adopters of graph databases, enabling more natural representation models and better data integration workflows, exploration and analysis facilities. In this work, we survey the literature to explore the evolution, performance and how the most recent graph database solutions are applied in the biomedical domain, compiling a great variety of use cases. With this evidence, we conclude that the available graph database management systems are fit to support data-intensive, integrative applications, targeted at both basic research and exploratory tasks closer to the clinic.
在过去的几十年中，密集互联数据的爆炸式增长刺激了图数据库技术的研究、开发和采用。从早期的图模型到最近的原生图数据库，实现的范围已经发展到涵盖企业级需求。由于其数据的互联性质，生物医学领域是图数据库的早期采用者之一，它支持更自然的表示模型和更好的数据集成工作流程、探索和分析工具。在这项工作中，我们调查了文献，以探索图数据库解决方案在生物医学领域的发展、性能和应用，收集了各种各样的用例。有了这些证据，我们得出结论，现有的图数据库管理系统能够支持数据密集型、集成应用，针对基础研究和更接近临床的探索性任务。
