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Towards a Digital Earth: using archetypes to enable knowledge interoperability within geo-observational sensor systems design

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Abstract

Earth System Science (ESS) observational data are often inadequately semantically enriched by geo-observational information systems to capture the true meaning of the associated data sets. Data models underpinning these information systems are often too rigid in their data representation to allow for the ever-changing and evolving nature of ESS domain concepts. This impoverished approach to observational data representation reduces the ability of multi-disciplinary practitioners to share information in a computable way. Object oriented techniques that are typically employed to model data in a complex domain (with evolving domain concepts) can unnecessarily exclude domain specialists from the design process, invariably leading to a mismatch between the needs of the domain specialists, and how the concepts are modelled. In many cases, an over simplification of the domain concept is captured by the computer scientist. This paper proposes that two-level modelling methodologies developed by health informaticians to tackle problems of domain specific use-case knowledge modelling can be re-used within ESS informatics. A translational approach to enable a two-level modelling process within geo-observational sensor systems design is described. We show how the Open Geospatial Consortium’s (OGC) Observations & Measurements (O&M) standard can act as a pragmatic solution for a stable reference-model (necessary for two-level modelling), and upon which more volatile domain specific concepts can be defined and managed using archetypes. A rudimentary use-case is presented, followed by a worked example showing the implementation methodology and considerations leading to an O&M based, two-level modelling design approach, to realise semantically rich and interoperable Earth System Science based geo-observational sensor systems.

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Abbreviations

ADL :

archetype description language

AM :

archetype model

AQL :

archetype query language

CEN :

european committee for standardization (comité européen de normalisation)

CEM :

clinical element model

COP :

common operational picture

DOLCE :

descriptive ontology for linguistic and cognitive engineering

ECHO :

earth observing system clearing house

EHR :

electronic healthcare record

EO :

earth observation

ESS :

earth system science

INSPIRE :

infrastructure for spatial information in europe

ISO :

international organization for standardization

JSON :

javascript object notation

MSDI :

marine spatial data infrastructure

NASA :

national aeronautics and space administration

NERC :

natural environment research council

NTNU :

norwegian university of science and technology

O&M :

observations and measurements

OGC :

open geospatial consortium

OPT :

operational templates

PSC :

planning support concept

RM :

reference model

SOS :

sensor observation service

SDI :

spatial data infrastructure

SSNO :

semantic sensor network ontology

SWEET :

semantic web for earth and environment technology

TC :

technical committee

tPOT :

towards people oriented technologies

XML :

extensible markup language

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Acknowledgements

The authors would like thank Adam Leadbetter from the Marine Institute, Ireland, for review comments and constructive feedback.

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  1. Institute of Technology Blanchardstown, Blanchardstown Road North, Dublin 15, D15 YV78, Ireland

    Paul Stacey

  2. Dublin Institute of Technology, Kevin Street, Dublin 8, D08 NF82, Ireland

    Paul Stacey & Damon Berry

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Correspondence to Paul Stacey.

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Communicated by: H. A. Babaie

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Stacey, P., Berry, D. Towards a Digital Earth: using archetypes to enable knowledge interoperability within geo-observational sensor systems design. Earth Sci Inform 11, 307–323 (2018). https://doi.org/10.1007/s12145-018-0340-z

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