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Architecture

Architecture plays a very important role in enabling interoperability. It describes the organizational structure of concepts, processes, and assets, including data and workflows. It comprises structural aspects of models and standards that govern the collection, storage, arrangement, integration, and use of data, based on which interoperability of data and services are built on.

Following Architecture has impact on Interoperbility

  1. Data Architecture
  2. Cloud Computing Architecture
  3. Data Analytics Architecture

Data Architecture

This section will talk about Archival and preservation .....

ID Recommendations
DSTOR#1
DSTOR#2
DSTOR#3
DSTOR#4
DSTOR#5
DSTOR#6

Cloud Computing Architecture

The Cloud computing architecture supports standardized API, protocols and provides capability for publishing resources as services, this enables seamless integration of data and services irrespective of the underlying architecture and location

ID Recommendations
CLOUD#1 The data to be shared through cloud should be converted to cloud optimized formats for faster and interoperable access across multiple applications
CLOUD#2
CLOUD#3
CLOUD#4
CLOUD#5
CLOUD#6

Analytics Architecture

Data Analytics architecture in an organization provides capability to store, analyze and visualize the data. As a typical Earth Observation data analytics require a large volume of time series data and hence it is necessary to support interoperability in the analytics architecture using following recommendations.

Analysis Ready Data

Analysis Ready Data (ARD) are starting point for interoperability in analysis and hence Data Providers are encouraged to develop ARD

ARD Recommendations

ID Recommendations
ARD#1 CEOS ARD Framework should be used as a starting point for development of Analysis Ready Data
ARD#2 CEOS Product Family Specifications (PFS) should be used for development of ARD products. In case if a new ARD is to be developed, use PFS template and submit to CEOS for approval
ARD#3 CEOS ARD compliance of the product requires two level of assessments, first is self assessment (CEOS ARD Self Assessment Guide) and second is peer review by CEOS Experts
ARD#4
ARD#5

Datacube

Data Cube provide capability to pack a collection of data and provide capability for fast access and analysis

Datacube Recommendations

ID Recommendations
DCUBE#1 The CEOS supported Open Data Cube can be taken as a reference for Data cube implementation
DCUBE#2 Data cubes should support spatial and/or temporal dimensions and capability for publishing available variables/properties as metadata
DCUBE#3 Data cubes should abstract the underlying data storage architecture to support hybrid data cubes and interoperability among different types of data cubes
DCUBE#4 Data cube may contain raw sensor data, analysis ready data or decision ready information. Analysis Reay Data should be preferred to avoid pre-processing overheads and fast analysis
DCUBE#5 Data cubes should be able to publish the supported pre-built analysis functions
DCUBE#6 Data Cubes should provide REST API and OGC web services based access for easy integration of end user applications

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