A Reference Model approach for developing agricultural big data infrastructures
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摘要: 连续的环境观测数据是复杂系统，如天气建模、智慧型科技创新和宏观系统层科学研究的重要驱动力；数据源、应用领域和应用需求的不同，使得管理千差万别的实时数据，并提供有效查找、识别、融合和重用服务变得极富挑战性.数据基础设施通过为数据提供全生命周期的管理服务，为上层数据驱动的科学研究和应用创新提供标准化的数据查询、读取和处理服务.然而数据基础设施的建设常局限于特定领域和历史遗留的前期系统，缺少统一参考，以及基础设施之间数据和服务的互通困难，极大地制约了上层应用，特别是跨学科研究发展的需求.针对大数据基础建设中的上述挑战，借鉴欧洲环境大数据参考模型在环境大数据基础设施建设方面的经验，提出了农业大数据参考模型，为我国农业大数据基础设施建设和所涉及的数据互通挑战提供参考.两个案例展示了所提出的农业大数据参考模型在大数据基础设施的需求分析改进、历史遗留系统的数据互通接入等方面的作用.Abstract: Big data infrastructures provide services for the management of data over the course of their lifecycle, and offer users the ability to effectively discover and access data for different application purposes. These emerging infrastructures essentially enable system-level data-centric research; third-party innovation, however, often requires data from different sources. The construction of big data infrastructures faces important interoperability challenges arising from the diverse nature of data acquisition, annotation, and identification performed in different research domains. Moreover, the evolution of different infrastructures is often driven by the specific interests of researchers, in their respective domains, and the constraints of legacy technology. The ENVRI Reference Model (ENVRI RM) is an output of the EU H2020 ENVRI and ENVRI PLUS project, targeting the aforementioned challenges in the context of environmental sciences by modeling environmental research infrastructures with a multi-viewpoints framework; these viewpoints include science, information, computation, engineering, and technology. Each viewpoint describes concrete aspects of a system definition and forms a mechanism to improve the interoperability across the whole system as well as alignment with existing legacy systems. The challenges encountered in the Shanghai Agricultural Big Data Infrastructures construction work are similar to those detected in the ENVRI RM, which provides an ideal place to test the generalizability of the ENVRI RM to other domains. Using the ENVRI RM as a reference, this paper presents an Agricultural Reference Model, which includes the five aforementioned viewpoints, but with consideration of the specifics of the agricultural domain, to address the problems encountered in revising and upgrading the Shanghai Agricultural Big Data Infrastructures. Two use cases are introduced to demonstrate its effectiveness. One is to improve the requirement engineering procedure with the community and role context captured using the Agricultural Reference Model. The other is to upgrade the large volume of existing systems to increase interconnections via the interoperability mechanisms provided by the Agricultural Reference Model.
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Key words:
- agriculture big data /
- reference model /
- infrastructure
2) 常称为e-Infrastructure或者cyber infrastructure3) 研究基础设施Research Infrastructure(RI)互用https://ec.europa.eu/research/infrastructures9) Virtual Research Environment -
图 6 农业大数据基础设施信息视角
Fig. 6 The information viewpoint of agricultural big data infrastructures
图 7 农业大数据基础设施数据状态和数据操作
Fig. 7 Data states and operations in agricultural big data infrastructures
图 8 参考模型在需求工程和设计中的作用
Fig. 8 The Reference Model's involvement in requirement engineering and system design
图 10 参考模型指导下的动态确定用户社群的需求分析
Fig. 10 Dynamic community requirement analysis supervised by the Reference Model
图 11 基于参考模型指导实现的用户社群和角色分析系统的运行界面截屏
Fig. 11 Screenshot of the Revised Community and Actor Analysis System
图 13 升级后的农机调度功能运行界面; 上半部分为日常管理界面图, 左下部分为作业面积测量, 右下部分为农机轨迹追踪(电子围栏)
Fig. 13 Screenshots of the Upgraded Agricultural Machinery Management System; the upper figure is the general user interface; the bottom left figure shows the automatic acres measurement function; and the bottom right is the trajectory analysis, which can be used for the electronic fencing function
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