@zazuko/query-rdf-data-cube

@zazuko/query-rdf-data-cube

Query and explore RDF Data Cubes with a JavaScript API, without writing SPARQL.

Installation

npm install @zazuko/query-rdf-data-cube

Documentation

See https://zazuko.github.io/query-rdf-data-cube/

Changelog: https://github.com/zazuko/query-rdf-data-cube/blob/master/CHANGELOG.md

Examples

See the examples/ folder at the root of this repository.

Working Locally

1. git clone
2. npm ci
3. npm run build

Running the Tests

• npm run test

Building and Publishing Documentation

1. npm run docs:compile
2. 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 Data Cube lets you access its DataSets:

const { DataCube } = require("query-rdf-data-cube");

// instantiate an RDF Data Cube with a SPARQL endpoint
const datacube = new DataCube("https://ld.stadt-zuerich.ch/query");

// fetch all its datasets
const datasets = await datacube.datasets();

// assign the 4th dataset
const dataset = datasets[3];

You could also directly instantiate a DataSet.

A DataSet can retrieve its Components, ie. its Dimensions, Measures and Attributes:

const dimensions = await dataset.dimensions();
const measures = await dataset.measures();
const attributes = await dataset.attributes();

const zeitDimension = dimensions[0];
// const raumDimension = dimensions[1];
// Again, dimensions, measures and attributes can also be constructed from scratch:
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 = dataset
  .query()
  // .select({}) takes binding names as keys and Component (Dimension/Attribute/Measure) as values
  .select({
    betriebsart: betriebsartDimension,
    geschlecht: geschlechtDimension,
    raum: raumDimension,
    zeit: zeitDimension,

    bep: beschaeftigteMeasure.avg(),

    // include some extra attributes, not all of them
    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();