A view-based model of data-cube to support big earth data systems interoperability

Figures & data

Figure 1. Modeling a data-cube cyber-infrastructure using interoperability views.

Table 1. Semantic View main characteristics.

View characteristics
Type	Data/information content
Concerns	Cube analytical purpose Information stored in the content Information encoded in each feature (i.e. thematic) “layer” of the cube – i.e. cube content and all necessary metadata, associated to them, to make them usable in a real application
Stakeholders	Earth System domain experts Community of Practices and Business case experts
Patterns	Data and information typing specifications – e.g. INSPIRE data definition schemas, CF conventions, EO Essential Variables, pre-defined domain ontologies (including Linked Data ones), etc Semantic and ontological languages to be used along to enrich and disambiguate content metadata – e.g. OWL, RDF, etc RDA PIT recommendations

Table 2. Geometry View main characteristics.

View characteristics
Type	Data/information content
Concerns	Content geometrical representation Content discretization and digital structuring – e.g. coverage geometry domain and co-domain, precision, uncertainty, etc Dimension neutrality and query optimization
Stakeholders	Business domain experts Geospatial information experts – e.g. coverage and feature modeling experts Data Hypercube experts and Business Intelligence professionals
Patterns	ISO-TC/OGC feature and coverage general models; netCDF-CDM, CF-CDM, OGC SWE-CDM Business intelligence and the online analytical processing multidimensional modeling – e.g. facts tables, star schemas, snowflakes schemas, and multi-cubes

Table 3. Encoding View main characteristics.

View characteristics
Type	Data/information content
Concerns	Hypercube (multidimensional) content encoding and storage – e.g. file formats, file systems, database structures, tiling strategy, etc Online analytical processing optimization Pre-processing and query optimization
Stakeholders	Multidimensional data storage experts Online analytical processing experts
Patterns	File systems and formats – e.g. OGC netCDF, HDF, GeoTIFF, OGC GML, ECMA JSON, W3C RDF, OGC GeoPackage Multidimensional DB – e.g. Relational DB, Array DB, Hybrid DB, column-oriented NoSQL DB Big data tiling strategies

Table 4. Interaction/interface view main characteristics.

View characteristics
Type	Cyberinfrastructure
Concerns	System functionality and accessibility by the User (analyst) Set of operations to allow data (and metadata) access, manipulation and analysis Web-based APIs
Stakeholders	Data analytics, Business Intelligence professionals, and multidimensional hypercube experts Web API experts Interoperability experts
Patterns	Web APIs (e.g. JSON/REST) Notebook Web applications (e.g. Jupyter and IPython) OLAP APIs (e.g.) OGC WCPS, OPeNDAP and Google Earth Engine analytical languages SITS, WTSS APIs proposed in the framework of OGC

Table 5. Interconnection/platform view main characteristics.

View characteristics
Type	Cyberinfrastructure
Concerns	Software components and services necessary to realize the cybernetic system System design and scalability Exposed APIs and control mechanisms
Stakeholders	Software system analyst and designers HPC Infrastructure experts SoS architecture interoperability Software pattern experts
Patterns	SoS patterns, and Software design patterns (e.g. Separation of Concerns, transparency, re-usability, decentralization) Cloud computing interoperability patterns

Table 6. Composition/Ecosystem View main characteristics.

View characteristics
Type	Cyberinfrastructure
Concerns	System Interoperability (e.g. representation of data defined on a spherical geometry domain), distribution, and scalability System governance
Stakeholders	SECO and SoS experts International standards experts Interoperability experts; system and policy managers Business Intelligence association, standardization organizations, and EO federations
Patterns	SECO patterns DGGS standardization process SoS Virtual architectures, collaborative SoS architectures, SoS Directed architecture SoS governance styles (i.e. directed, collaborative, acknowledged, virtual)

Data availability statement

Data sharing is not applicable to this article as no new data were created or analysed in this study.