Query and explore RDF Data Cubes with a JavaScript API,
without writing SPARQL.
Installation
npm install @zazuko/query-rdf-data-cube
- CHANGELOG
Concepts
RDF Data Cubes are an implementation of the generic OLAP concept and more specifically, OLAP cube.
What RDF Data Cube calls data structure definition can be mapped to dimension tables in OLAP cubes, the observations in RDF Data Cube are called fact tables.
The main classes this library exposes are the following:
DataCubeEntryPoint
The EntryPoint has a SPARQL endpoint.
It lets you discover and fetch Data Cubes available at this endpoint and their corresponding Dimensions, Attributes, and Measures.DataCube
A Data Cube represents the cube model. It has Dimensions, Attributes and Measures and is initialized with an IRI and a graph IRI.
Querying a DataCube starts with having a DataCube instance.Component
An abstract class, parent of the following component properties:
Filter operators can be applied directly to components.
API Documentation
See https://zazuko.github.io/query-rdf-data-cube/
Examples
See the examples/
folder at the root of this repository.
Working Locally
git clone
npm ci
npm run build
Running the Tests
Building and Publishing Documentation
npm run docs:compile
npm run docs:publish
Running the Examples
node examples/introspect-and-query.js
Features and Usage
This library has two main use cases: exploring an RDF Data Cube and querying an RDF Data Cube.
Both usually go hand in hand.
Instantiating a DataCube EntryPoint lets you access its DataCubes:
const { DataCubeEntryPoint } = require("query-rdf-data-cube");
const entryPoint = new DataCubeEntryPoint("https://ld.stadt-zuerich.ch/query");
const dataCubes = await entryPoint.dataCubes();
const datacube = dataCubes[3];
You could also directly instantiate a DataCube.
A DataCube can retrieve its Components, ie. its Dimensions, Measures and Attributes:
const dimensions = await datacube.dimensions();
const measures = await datacube.measures();
const attributes = await datacube.attributes();
const zeitDimension = dimensions[0];
const raumDimension = new Dimension({
label: "Raum",
iri: "https://ld.stadt-zuerich.ch/statistics/property/RAUM"
});
const betriebsartDimension = dimensions[2];
const geschlechtDimension = dimensions[3];
const beschaeftigteMeasure = measures[0];
const quelleAttribute = attributes[0];
const glossarAttribute = attributes[1];
const erwarteteAktualisierungAttribute = attributes[4];
const korrekturAttribute = attributes[5];
This setup is quite exhaustive, in many situations you won't want to create all of these or get all of these and rely on this lib to properly guess what to query.
const query = datacube
.query()
.select({
betriebsart: betriebsartDimension,
geschlecht: geschlechtDimension,
raum: raumDimension,
zeit: zeitDimension,
bep: beschaeftigteMeasure.avg(),
quelle: quelleAttribute,
glossar: glossarAttribute,
erwarteteAktualisierung: erwarteteAktualisierungAttribute,
korrektur: korrekturAttribute,
})
.filter(raumDimension.equals("https://ld.stadt-zuerich.ch/statistics/code/R30000"))
.groupBy("zeit")
.having(({ bep }) => bep.gte(10000))
.limit(3);
Now that we built a query, we can generate SPARQL out of it:
const sparql = await query.toSparql();
Or execute the SPARQL query against the SPARQL endpoint:
const results = await query.execute();