Data architecture Sample Clauses
The Data Architecture clause defines the structure, organization, and management of data within a system or project. It typically outlines how data is collected, stored, accessed, and integrated across various platforms or applications, and may specify standards for data formats, security, and interoperability. By establishing clear guidelines for data handling and system integration, this clause ensures consistency, efficiency, and compliance with relevant data governance requirements, ultimately reducing the risk of data silos and mismanagement.
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Data architecture. The deliverable D2.3 highlighted the requirement for a generalised asset descriptor for search purposes. In this specification, a recommendation was put forward for a canonical data format and common data model. The basis of this model was RDF (Resource Description Framework), with domain vocabularies represented using RDFS (Resource Description Framework Schema) and OWL (Web Ontology Language), and serialised as JSON-LD. It is this serialised document that is passed through the ingestion process, with different micro applications (classifiers, transformers, etc) decorating and enriching it accordingly, all the way through to the point that is persisted in the search engine. JSON-LD is the only supported serialisation, since it is both OpenWhisk conformant and RDF conformant. However, all JSON-LD sub-serialisations are supported (compact, embedded, flattened, etc). An Ingest document can contain information about one or more assets, identified using combinations of URIs originating from one or more systems. In addition to this, T-Box data can also be included, representing either logical rules, which should be applied during ingestion, or additional properties derived from enrichment. To enable a better understanding of the Generalised descriptor, the framework includes: ● A set of core vocabularies. ● A SHACL (Shapes Constraint Language) document explaining the shape of an ingest document. ● A breakdown of the anatomy of an ingest document.
Data architecture. This chapter describes the content of the PATHS system architecture data layer and the virtual data repository consisting of a file store, an RDBMS and an RDF store.
Data architecture. The application addresses the analysis of “big data” through supervised and unsupervised machine learning models. In order to align, store, access, analyze and synchronize all these attributes we need to create a strong data platform that supports both relational database management system (R DBMS) as well as big non-relational data. The theoretical architecture of this platform will be in a cylindrical design with RDBMS at the core to handle the Relational database with Apache Hadoop surrounding it to analyze and deliver Non-Relational, NoSQL data to API/data warehouses on the surface. The idea is to create a strong table-based database for the main elements of the initiative. Collecting and processing the data from different sources will be Hadoop based. For the IT Infrastructure backbone of the "Trial on FHIR” application development we will be using an end-to-end cloud (hosted) platform. Primarily our solution is IaaS (Infrastructure as a Service) based architecture which is a proven scalable, flexible and cost-effective solution. Our vendor of choice will be Amazon Web Services (AWS).