Statistical Cubes with RDF
A Visual Guide to RDF Data Cubes and their Visualization
What is an RDF Data Cube?
An RDF Data Cube is a structured way to represent multi-dimensional statistical data using the Resource Description Framework (RDF). It's based on the W3C RDF Data Cube Vocabulary, which provides a standard model for data interoperability.
Think of it as a spreadsheet with three core components, represented as RDF triples:
- Dimensions: The descriptive categories that define each data point. For example, `country` and `year`. These are like the column headers in your spreadsheet.
- Measures: The quantitative values being measured. In our example, `gdpUSD` is the measure. This is the actual data in the cells.
- Observations: The individual data points that combine specific values for each dimension to yield a measure. An observation links a country and a year to a specific GDP value.
Why Use RDF for Statistical Cubes?
Using RDF for data cubes provides significant benefits, primarily due to the nature of Linked Data.
- Interoperability: RDF provides a common language and model, allowing data to be easily shared and combined with other datasets on the web.
- Semantic Richness: Dimensions, measures, and attributes are defined with clear URIs (unique identifiers), giving them meaning and context. For example, `ex:country/USA` can be a link to more information about the United States from other sources.
- Flexibility: The model is flexible. You can easily add new dimensions or attributes without altering the entire structure.
- Powerful Querying: Data can be queried using SPARQL, a powerful query language for RDF. This allows for complex joins, filtering, and aggregation that go beyond simple SQL-like queries.
How It Works: RDF & SPARQL
The process involves defining the data structure and then representing each observation as a series of RDF triples.
1. The RDF (Turtle) Example:
This RDF defines the dataset, its structure (DSD), and the individual observations linking countries and years to GDP values.
2. The SPARQL Queries:
These queries demonstrate how you can extract and manipulate the data.
List Observations (with Country Names)
Pivot-like Summary (Latest GDP)