华东师范大学学报(自然科学版) ›› 2019, Vol. 2019 ›› Issue (2): 77-96.doi: 10.3969/j.issn.1000-5641.2019.02.009

• 计算机科学 • 上一篇    下一篇

农业大数据基础设施开发的参考模型方法

赵志明1, 廖晓锋1, 王晓玲2, 阮春晟3, 朱轶峰3, 丰东升4   

  1. 1. 阿姆斯特丹大学 信息技术研究所, 阿姆斯特丹 1098 XH;
    2. 华东师范大学 上海市高可信计算重点实验室, 上海 200062;
    3. 上海农业物联网工程技术研究中心, 上海 200050;
    4. 上海市农产品质量安全中心, 上海 200003
  • 收稿日期:2018-03-22 出版日期:2019-03-25 发布日期:2019-03-27
  • 作者简介:赵志明,男,博士,研究员,研究方向为云计算、大数据管理、软件工程.E-mail:zmzhao@ieee.org.
  • 基金资助:
    上海市科技兴农重点攻关项目（沪农科攻字（2016）第2-1号）

A Reference Model approach for developing agricultural big data infrastructures

ZHAO Zhi-ming1, LIAO Xiao-feng1, WANG Xiao-ling2, RUAN Chun-sheng3, ZHU Yi-feng3, FENG Dong-sheng4   

  1. 1. Informatics Institute, University of Amsterdam, 1098 XH Amsterdam, Netherlands;
    2. Shanghai Key Laboratory of Trustworthy Computing, East China Normal University, Shanghai 200062, China;
    3. Shanghai Agriculture Internet of Thing(IoT) Development and Research Center, Shanghai 200050, China;
    4. Shanghai Agriculture Products Quality and Safety Center, Shanghai 200003, China
  • Received:2018-03-22 Online:2019-03-25 Published:2019-03-27

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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.

关键词: 农业大数据, 参考模型, 基础设施

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.

Key words: agriculture big data, reference model, infrastructure

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