Complete Modelling

The basic modelling process (identify, model, refine, review, map) can be repeated several times to obtain models covering complementary design concerns. The point at which the process should stop varies according to the intended use of the models (documentation, reporting, validation, etc.). The modellers should evaluate the benefits of creating models for each viewpoint with the rest of the stakeholders and stop the modelling process once a sufficiently fit for the purpose set of models has been obtained (Fig.17).

5 Outlook

The ENVRI RM was designed and developed to support understanding emerging and established research infrastructures, and their operation environments (processes, sys-tems and assets). The main goals of this research effort were to (1) discover common operations, (2) describe the systems and services which they provide and depend-on, and

Fig. 17. Engineering Viewpoint Model of the Catalogue Export Service. This model includes the three components used in the corresponding computational model shown in Fig.13.

(3) identify the requirements and challenges of integrating (required services, standards, and coordination).

The recommendation for the engineering viewpoint follows a microservice architec-ture model which allows the definition API interfaces that support flexible integration of services and systems. The recommendation for the Technology Viewpoint allows the use of templates for defining conformance points to verify the suitability of technologies and standards.

The ENVRI RM serves as a reference architecture for the evolution of the services offered and consumed by different research infrastructures into a coherent software product line. During the past years, ENVRI RM has not only been used by the RIs within ENVRIplus projects, but also application outside, e.g. for a Chinese agricultural data management infrastructure [22]. This software product line can facilitate:

• Creating client libraries for commonly used services Identifier services are a good use case, they are likely to connect to existing third-party Services (ORICID, DOI and ePIC.);

• Creating service Templates for commonly implemented services. Cross-cutting ser-vices such as cataloguing, provenance, processing, and AAAI serser-vices are candidates for service templates;

• Creating engineering tools supporting the selection and use of services;

Facilitating the profiling of exiting complex solutions which may be considered for adoption, for instance, VRE implementations.

Acknowledgements. This work was supported by the European Union’s Horizon 2020 research and innovation programme via the ENVRIplus project under grant agreement No 654182.

References

1. Zhao, Z., Grosso, P., de Laat, C.: OEIRM: an open distributed processing based interoper-ability reference model for e-science. In: Park, J.J., Zomaya, A., Yeo, S.-S., Sahni, S. (eds.) NPC 2012. LNCS, vol. 7513, pp. 437–444. Springer, Heidelberg (2012).https://doi.org/10.

1007/978-3-642-35606-3_52

2. ENVRI Project: Common Operations of Environmental Research Infrastructures. Grant agreement ID: 283465. FP7 programme. Start: 01 November 2011 End: 31 October 2014.

https://cordis.europa.eu/project/rcn/101244/factsheet/en

3. ENVRIplus Project: Environmental Research Infrastructures Providing Shared Solutions for Science and Society. Grant agreement ID: 654182. H2020 programme. Start: 01 May 2015 End: 31 July 2019.https://cordis.europa.eu/project/rcn/194947/factsheet/en

4. Zhao, Z., et al.: Reference model guided system design and implementation for interoperable environmental research infrastructures. In: 2015 IEEE 11th International Conference on e-Science, Munich, Germany, pp. 551–556. IEEE (2015).https://doi.org/10.1109/eScience.201 5.41

5. IISO/IEC 10746-1:1998 Information technology – Open Distributed Processing – Reference model: Overview

6. ISO/IEC 10746-2:2009 Information technology – Open Distributed Processing – Reference model: Foundations

7. ISO/IEC 10746-3:2009 Information technology – Open Distributed Processing – Reference model: Architecture

8. ISO/IEC 10746-4:1998 Information technology – Open Distributed Processing – Reference model: Architecture Semantics

9. Linington, P.F., Milosevic, Z., Tanaka, A., Vallecillo, A.: Building Enterprise Systems with ODP. CRC Press, Boca Raton (2012)

10. IEEE, 1471-2000, IEEE Recommended Practice for Architectural Description of Software Intensive Systems (2000)

11. ISO/IEC/IEEE Standard 42010:2011 Systems and Software Engineering – Architecture description

12. Martin, P., et al.: Open information linking for environmental research infrastructures. In:

2015 IEEE 11th International Conference on e-Science, Munich, Germany, pp. 513–520.

IEEE (2015).https://doi.org/10.1109/eScience.2015.66

13. Guo, Y.K.: Assimilated learning-bridging the gap between big data and smart data. In IoTBDS, p. 7 (2017)

14. Snijders, C., Matzat, U., Reips, U.D.: Big data: big gaps of knowledge in the field of internet science. Int. J. Internet Sci.7(1), 1–5 (2012)

15. Dedi´c, N., Stanier, C.: Towards differentiating business intelligence, big data, data analytics and knowledge discovery. In: Piazolo, F., Geist, V., Brehm, L., Schmidt, R. (eds.) ERP Future 2016. LNBIP, vol. 285, pp. 114–122. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-58801-8_10

16. UK Data Service: Research Data Lifecycle.https://www.ukdataservice.ac.uk/manage-data/

lifecycle.aspx. Accessed 28 Apr 2019

17. Digital Curation Centre: Curation Lifecycle Model. http://www.dcc.ac.uk/resources/curation-lifecycle-model. Accessed 28 Apr 2019

18. Data One: Data Life Cycle.https://www.dataone.org/data-life-cycle. Accessed 28 Apr 2019 19. Rec. ITU-T X.906 (10/2014) | ISO/IEC 19793 ITU-T: 2015 Information technology – Open

Distributed Processing – Use of UML for ODP system specifications

20. Nieva de la Hidalga, A., Hardisty, A., Magagna, B., Martin, P., Zhao, Z.: Use of the ENVRI Reference Model to Support the Design of Environmental Research Infrastructures.

Geophysical Research Abstracts, vol. 20, EGU2018-18552, 2018 EGU General Assembly (2018)

21. EPOS: Integrated Core Services Architecture.https://www.epos-ip.org/data-services/ict-arc hitecture/ics-architecture. Accessed 28 Apr 2019

22. Zhao, Z., Liao, X., Wang, X., Ruan, C., Zhu, Y., Feng, D.: A Reference Model approach for developing agriculture big data infrastructures. J. East China Normal Univ. (Nat. Sci.) (2) 77–96 (2019).http://xblk.ecnu.edu.cn/EN/10.3969/j.issn.1000-5641.2019.02.009

Open Access This chapter is licensed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

The images or other third party material in this chapter are included in the chapter’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the chapter’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder.

Zhiming Zhao¹⁽B⁾ and Keith Jeffery²

1 Multiscale Networked Systems, University of Amsterdam, 1098XH Amsterdam, The Netherlands

z.zhao@uva.nl

2Keith G Jeffery Consultants, Faringdon, UK

keith.jeffery@keithgjefferyconsultants.co.uk

Abstract. Environmental research infrastructures (RIs) support their respective research communities by integrating large-scale sensor/observation networks with data curation and management services, analytical tools and common operational policies. These RIs are developed as service pillars for intra- and interdisciplinary research; however, comprehension of the complex, interconnected aspects of the Earth’s ecosystem increasingly requires that researchers conduct their experiments across infrastructure boundaries. Consequently, almost all data-related activities within these infrastructures, from data capture to data usage, need to be designed to be broadly interoperable in order to enable real interdisciplinary innovation and to improve service offerings through the development of common services. To address these interoperability challenges as they relate to the design, implementa-tion and operaimplementa-tion of environmental RIs, a Reference Model guided engineering approach was proposed and has been used in the context of the ENVRI cluster of RIs. In this chapter, we will discuss how the approach combines the ENVRI Ref-erence Model with the practices of Agile systems development to design common data management services and to tackle the dynamic requirements of research infrastructures.

Keywords:Research infrastructure·Reference Model·Interoperability·Agile

1 Introduction

Many key problems in environmental science are intrinsically interdisciplinary; the study of climate change, for example, involves the study of the atmosphere, but also earth pro-cesses, the oceans and the biosphere. Modelling these processes individually is difficult enough, but modelling their interactions is another order of complexity entirely. Scien-tists are challenged to collaborate across conventional disciplinary boundaries, but must first discover, extract and understand data dispersed across many different sources and formats.

Data-centric research differs from classical approaches for analytical modelling or computer simulation insofar as new theories are measured first and foremost against huge quantities of observations, measurements, documents and other data sources culled from

© The Author(s) 2020

Z. Zhao and M. Hellström (Eds.): Towards Interoperable Research

Infrastructures for Environmental and Earth Sciences, LNCS 12003, pp. 82–99, 2020.

https://doi.org/10.1007/978-3-030-52829-4_5

a range of possible sources. To enable such science, the underlying research infrastructure must provide not only the necessary tools for data discovery, access and manipulation but also facilities to enhance collaboration between scientists of different backgrounds.

Environmental research infrastructures (RIs) support user communities by providing federated data curation, discovery and access services, analytical tools and common oper-ational policies integrated around large-scale sensor/observer networks, often deployed on a continental scale. Examples in Europe include LifeWatch¹(concerned with bio-diversity), EPOS²(solid Earth science), Euro-Argo³and EMSO⁴(ocean monitoring), as well as ICOS⁵ and the new EISCAT_3D system (atmosphere)⁶. These infrastruc-tures are developing into important pillars for their respective user communities, but are also intended to support interdisciplinary research as well as more specific research data aggregators such as Copernicus⁷within the context of GEOSS⁸. As such, it is very important that data-related activities are well integrated in order to enable data-driven system-level science [2]. This requires standard policies, models and e-infrastructure to improve technology reuse and ensure coordination, harmonization, integration and interoperability of data, applications and other services. However, the complex nature of environmental science seems to result in the development of environmental RIs that meet only the requirements and needs of their own specific domains, with very limited interoperability of data, services, and operation policies among infrastructures.

It is thus important to identify technical and organizational commonalities for the cluster of research infrastructures in environmental and Earth sciences and provide a unified data discovery and access services to the whole RI activity cycle. This chapter presents the engineering model developed in the EU H2020 projects ENVRI, ENVRIplus and ENVRI-FAIR [3] for 1) combining both domain-specific characteristics and common abstractions; 2) harmonising RI-specific requirements with common operations; and 3) accounting for both existing generic e-infrastructures already adopted by existing RIs.

The chapter is an extension of the earlier publication in IEEE eScience 2015 [1].