Explore a full suite of tools & services designed to help you define, describe, and transform your datasets into interoperable DataCubes. From building structured Data Structure Definitions and managing the Data Model Components and Codelists, to RDFising and annotating with well-known vocabularies your datasets, CubeModeler will assist you in every step. Browse your own catalogs of data structures definitions, datasets and codelists, and bring consistency and connectivity to your data lifecycle and data integration needs.
How CubeModeler works
CubeModeler supports a modeling-based workflow for transforming statistical tabular datasets into interoperable RDF cubes. The workflow follows six main steps:
Step 1: Make a Data Structure Definition
Define the structure of the RDF cube by selecting dimensions, measures, and attributes from a data model.
Step 2: Update the Data Model
Basides reusing existing components when possible, you can also define new dimensions, measures, or attributes when the dataset requires more specific modeling. Validate new components and edit the relations between them in the data model hierarchy.
Step 3: Import or create codelists
Connect coded dimensions and attributes to your controlled SKOS codelists, or create new codelists from dataset values.
Step 4: Describe the dataset
Add dataset-level metadata, including the dataset IRI, label, relevant DSD, provenance, distribution, version, licence information and more.
Step 5: RDFize the dataset
Map source columns and fixed values to the selected DSD components. CubeModeler generates RDF/Turtle output, validates it and prepares it for publication.
Step 6: Query and integrate
Upload the generated RDF resources to the knowledge graph and use SPARQL queries to retrieve integrated results across datasets, DSDs, codelists and shared or related components.
View more at the illustrative workflow page
Step 1: Make a Data Structure Definition
Define the structure of the RDF cube by selecting dimensions, measures, and attributes from a data model.
Step 2: Update the Data Model
Basides reusing existing components when possible, you can also define new dimensions, measures, or attributes when the dataset requires more specific modeling. Validate new components and edit the relations between them in the data model hierarchy.
Step 3: Import or create codelists
Connect coded dimensions and attributes to your controlled SKOS codelists, or create new codelists from dataset values.
Step 4: Describe the dataset
Add dataset-level metadata, including the dataset IRI, label, relevant DSD, provenance, distribution, version, licence information and more.
Step 5: RDFize the dataset
Map source columns and fixed values to the selected DSD components. CubeModeler generates RDF/Turtle output, validates it and prepares it for publication.
Step 6: Query and integrate
Upload the generated RDF resources to the knowledge graph and use SPARQL queries to retrieve integrated results across datasets, DSDs, codelists and shared or related components.
View more at the illustrative workflow page
Tools & Services
Select components (dimensions, measures, attributes) from the UPCAST enviromental data models to build your specific Data Structure Definition (DSD)
Describe your datasets with various metadata from multiple vocabularies (UPCAST, DCAT, DCMI, PROV and others) for clarity and interoperability
The CubeModeler RDFiser will guide you to transform your dataset into RDF/TTL format based on your mapping and the dataset requirements
Create a new component for the UPCAST data model, in case it seems more suitable than the current components
Assign additional relations to your data model components
Activate and edit your data model components
Import a new Codelist, related to a CubeModeler component
Create a new Codelist directly from your dataset.
Catalogs
Explore, edit and download all your available Data Structure Definitions
Browse your described Datasets, preview, edit and download all relevant info
Check code definitions, descriptions, and categories of the CubeModeler Codelists for your dataset needs
Knowledge Graph statistics (number of entities, dimensions used etc)
Quick start
This demo instance is provided to illustrate the CubeModeler workflow.
Reviewers/Users can use the demo to inspect how CubeModeler supports DSD construction, component and codelist management, dataset description, RDF transformation, validation and publication to a knowledge graph.
Data models, as well as supporting datasets, RDF outputs, DSDs, codelists and SPARQL queries are available in the accompanying GitLab resources repository. These resources can be used to inspect and reproduce the basketball and environmental & public administration use cases.
SPARQL access to query the Knowledge Graphs is provided in the Gitlab repository as well.
This demo instance is provided to illustrate the CubeModeler workflow.
Reviewers/Users can use the demo to inspect how CubeModeler supports DSD construction, component and codelist management, dataset description, RDF transformation, validation and publication to a knowledge graph.
Data models, as well as supporting datasets, RDF outputs, DSDs, codelists and SPARQL queries are available in the accompanying GitLab resources repository. These resources can be used to inspect and reproduce the basketball and environmental & public administration use cases.
SPARQL access to query the Knowledge Graphs is provided in the Gitlab repository as well.