ASTRIIS develops a data centre to store, process and share ocean information

Despite all the attention the ocean and marine science have received in recent years, a few major issues remain to solve: how to get all the ocean data needed and then how to store, share, and exploit it in sustainable ways?

The ASTRIIS project intends to acquire, value, and make use of ocean in-situ and remote data in several ways. The increase of ocean data and knowledge is expected to leverage several areas, such as research, innovation, tourism, renewable energy, aquaculture, and maritime safety while it is also expected to contribute to respond to climate change, marine pollution, and various other ocean hazards. When used, for example, in data science, artificial intelligence (AI), machine learning, numerical modelling, and geographical information systems (GIS), the potential of ocean data reaches, indeed, unheard levels.

ASTRIIS will acquire ocean data in situ through various activities including, for example, marine animal tags, multi-robot systems, autonomous surface vehicle, among others.  The project will also make use of data sources coming from public ocean initiatives, such as Copernicus, EUMETSAT, and NOAA. One of the key issues of ASTRIIS was therefore about how to handle such volume of data.

The ASTRIIS solution is based on the development of a data centre that will be more than a conventional data repository. Under the leadership of CEiiA, the project will build a digital platform for the upcoming years which will store considerable amounts of ocean data, making it accessible to supply several users’ demands.

Data centres are commonly defined as electronic devices used for data processing (using servers), data storage (storage equipment), and communication (network equipment) capable of processing, storing, and transmitting digital information. They are known by numerous names depending on the purpose: data farms, data hubs, data warehouses, data halls, AI labs.

The development of the ASTRIIS data centre consists of a four-step methodology, namely descriptive analytics, diagnostic analytics, predictive analytics, and prescription analytics. The platform will allow easy and intuitive access to data in different maturity levels. The platform considers a service-based architecture and, a microservice-based development capable of receiving various types of data from several sources, allowing to create distinct products for different customers/stakeholders.

The platform follows a typical software web development, having a Back-End (data acquisition from many sources), an ETL (Extract, Transform and Load), Intelligence (data science), and a Front-End (web dashboard) development. Besides having access to large amounts of data on a single platform, the users will be able to correlate different data layers from various sources using various AI models.

Would you like to know more about the data layers or interaction options? Drop us an email to astriis-coordination@tekever.com.