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 [![codecov](https://codecov.io/gh/Callidon/sparql-engine/branch/master/graph/badge.svg)](https://codecov.io/gh/Callidon/sparql-engine) [![npm version](https://badge.fury.io/js/sparql-engine.svg)](https://badge.fury.io/js/sparql-engine) [![JavaScript Style Guide](https://img.shields.io/badge/code_style-standard-brightgreen.svg)](https://standardjs.com)\n\nAn open-source framework for building SPARQL query engines in Javascript/Typescript.\n\n[Online documentation](https://callidon.github.io/sparql-engine/)\n\n**Main features**:\n* Build a [SPARQL](https://www.w3.org/TR/2013/REC-sparql11-overview-20130321/) query engine on top of any data storage system.\n* Supports [the full features of the SPARQL syntax](https://www.w3.org/TR/sparql11-query/) by *implementing a single class!*\n* Support for all [SPARQL property Paths](https://www.w3.org/TR/sparql11-query/#propertypaths).\n* Implements advanced *SPARQL query rewriting techniques* for transparently optimizing SPARQL query processing.\n* Supports [full text search queries](#full-text-search).\n* Supports [Custom SPARQL functions](#custom-functions).\n* Supports [Semantic Caching](#enable-caching), to speed up query evaluation of reccurent patterns.\n* Supports the [SPARQL UPDATE protocol](https://www.w3.org/TR/2013/REC-sparql11-update-20130321/).\n* Supports Basic [Federated SPARQL queries](https://www.w3.org/TR/2013/REC-sparql11-federated-query-20130321/) using **SERVICE clauses**.\n* Customize every step of SPARQL query processing, thanks to *a modular architecture*.\n* Support for [SPARQL Graph Management protocol](https://www.w3.org/TR/2013/REC-sparql11-update-20130321/#graphManagement).\n\n# Table of contents\n* [Installation](#installation)\n* [Getting started](#getting-started)\n  * [Examples](#examples)\n  * [Preliminaries](#preliminaries)\n  * [RDF Graphs](#rdf-graphs)\n  * [RDF Datasets](#rdf-datasets)\n  * [Running a SPARQL query](#running-a-sparql-query)\n* [Enable caching](#enable-caching)\n* [Full text search](#full-text-search)\n* [Federated SPARQL Queries](#federated-sparql-queries)\n* [Custom Functions](#custom-functions)\n* [Advanced Usage](#advanced-usage)\n  * [Customize the pipeline implementation](#customize-the-pipeline-implementation)\n  * [Customize query execution](#customize-query-execution)\n* [Documentation](#documentation)\n* [Aknowledgments](#aknowledgments)\n* [References](#references)\n\n# Installation\n\n```bash\nnpm install --save sparql-engine\n```\n\n# Getting started\n\nThe `sparql-engine` framework allow you to build a custom SPARQL query engine on top of any data storage system.\n\nIn short, to support SPARQL queries on top of your data storage system, you need to:\n* [Implements a subclass of `Graph`](#rdf-graphs), which provides access to the data storage system.\n* Gather all your Graphs as a `Dataset` (using your own implementation or [the default one](#rdf-datasets)).\n* [Instantiate a `PlanBuilder`](#running-a-sparql-query) and use it to execute SPARQL queries.\n\n## Examples\n\nAs a starting point, we provide you with two examples of integration:\n* With [N3.js](https://github.com/rdfjs/N3.js), available [here](https://github.com/Callidon/sparql-engine/tree/master/examples/n3.js).\n* With [LevelGraph](https://github.com/levelgraph/levelgraph), available [here](https://github.com/Callidon/sparql-engine/tree/master/examples/levelgraph.js).\n\n## Preliminaries\n\n### SPARQL.js algebra and TypeScript\n\nThe `sparql-engine` framework use the [`SPARQL.js`](https://github.com/RubenVerborgh/SPARQL.js/) library for parsing and manipulating SPARQL queries as JSON objects. For TypeScript compiltation, we use a custom package [`sparqljs-legacy-type`](https://github.com/Callidon/sparqljs-legacy-type) for providing the types information. \n\nThus, **if you are working with `sparql-engine` in TypeScript**, you will need to install the [`sparqljs-legacy-type`](https://github.com/Callidon/sparqljs-legacy-type) package.\n\nIf want to know why we use a custom types package, see [the discussion of this issue](https://github.com/Callidon/sparql-engine/issues/58).\n\n### RDF triples representation\n\nThis framework represents RDF triples using Javascript Object.\nYou will find below, in Java-like syntax, the \"shape\" of such object.\n\n```typescript\ninterface TripleObject {\n  subject: string; // The Triple's subject\n  predicate: string; // The Triple's predicate\n  object: string; // The Triple's object\n}\n```\n\n### PipelineStage\n\nThe `sparql-engine` framework uses a pipeline of iterators to execute SPARQL queries. Thus, many methods encountered in this framework needs to return `PipelineStage<T>`, *i.e.*, objects that generates items of type `T` in a pull-based fashion.\n\nAn `PipelineStage<T>` can be easily created from one of the following:\n* An **array** of elements of type `T`\n* A [**Javascript Iterator**](https://developer.mozilla.org/en-US/docs/Web/JavaScript/Reference/Iteration_protocols), which yields elements of type `T`.\n* An [**EventEmitter**](https://nodejs.org/api/events.html#events_class_eventemitter) which emits elements of type `T` on a `data` event.\n* A [**Readable stream**](https://nodejs.org/api/stream.html#stream_readable_streams) which produces elements of type `T`.\n\nTo create a new `PipelineStage<T>` from one of these objects, you can use the following code:\n```javascript\nconst { Pipeline } = require('sparql-engine')\n\nconst sourceObject = // the object to convert into a PipelineStage\n\nconst stage = Pipeline.getInstance().from(sourceObject)\n```\n\nFore more information on how to create and manipulate the pipeline, please refers to the documentation of [`Pipeline`](https://callidon.github.io/sparql-engine/classes/pipelinee.html) and [`PipelineEngine`](https://callidon.github.io/sparql-engine/classes/pipelineengine.html).\n\n## RDF Graphs\n\nThe first thing to do is to implement a subclass of the `Graph` abstract class. A `Graph` represents an [RDF Graph](https://www.w3.org/TR/rdf11-concepts/#section-rdf-graph) and is responsible for inserting, deleting and searching for RDF triples in the database.\n\nThe main method to implement is `Graph.find(triple)`, which is used by the framework to find RDF triples matching\na [triple pattern](https://www.w3.org/TR/sparql11-query/#basicpatterns) in the RDF Graph.\nThis method must return an `PipelineStage<TripleObject>`, which will be consumed to find matching RDF triples. You can find an **example** of such implementation in the [N3 example](https://github.com/Callidon/sparql-engine/tree/master/examples/n3.js).\n\nSimilarly, to support the [SPARQL UPDATE protocol](https://www.w3.org/TR/2013/REC-sparql11-update-20130321/), you have to provides a graph that implements the `Graph.insert(triple)` and `Graph.delete(triple)` methods, which insert and delete RDF triple from the graph, respectively. These methods must returns [Promises](https://developer.mozilla.org/en-US/docs/Web/JavaScript/Reference/Global_Objects/Promise), which are fulfilled when the insertion/deletion operation is completed.\n\nFinally, the `sparql-engine` framework also let your customize how [Basic graph patterns](https://www.w3.org/TR/2013/REC-sparql11-query-20130321/#BasicGraphPatterns) (BGPs) are evaluated against\nthe RDF graph. The engine provides a **default implementation** based on the `Graph.find` method and the\n*Index Nested Loop Join algorithm*. However, if you wish to supply your own implementation for BGP evaluation, you just have to implement a `Graph` with an `evalBGP(triples)` method.\nThis method must return a `PipelineStage<Bindings>`. You can find an example of such implementation in the [LevelGraph example](https://github.com/Callidon/sparql-engine/tree/master/examples/levelgraph.js).\n\nYou will find below, in Java-like syntax, an example subclass of a `Graph`.\n```typescript\n  const { Graph } = require('sparql-engine')\n\n  class CustomGraph extends Graph {\n    /**\n     * Returns an iterator that finds RDF triples matching a triple pattern in the graph.\n     * @param  triple - Triple pattern to find\n     * @return An PipelineStage which produces RDF triples matching a triple pattern\n     */\n    find (triple: TripleObject, options: Object): PipelineStage<TripleObject> { /* ... */ }\n\n    /**\n     * Insert a RDF triple into the RDF Graph\n     * @param  triple - RDF Triple to insert\n     * @return A Promise fulfilled when the insertion has been completed\n     */\n    insert (triple: TripleObject): Promise { /* ... */ }\n\n    /**\n     * Delete a RDF triple from the RDF Graph\n     * @param  triple - RDF Triple to delete\n     * @return A Promise fulfilled when the deletion has been completed\n     */\n    delete (triple: : TripleObject): Promise { /* ... */ }\n  }\n```\n\n## RDF Datasets\n\nOnce you have your subclass of `Graph` ready, you need to build a collection of RDF Graphs, called a [RDF Dataset](https://www.w3.org/TR/rdf11-concepts/#section-dataset). A default implementation, `HashMapDataset`, is made available by the framework, but you can build your own by subclassing [`Dataset`](https://callidon.github.io/sparql-engine/classes/dataset.html).\n\n```javascript\n const { HashMapDataset } = require('sparql-engine')\n const CustomGraph = require(/* import your Graph subclass */)\n\n const GRAPH_A_IRI = 'http://example.org#graph-a'\n const GRAPH_B_IRI = 'http://example.org#graph-b'\n const graph_a = new CustomGraph(/* ... */)\n const graph_b = new CustomGraph(/* ... */)\n\n // we set graph_a as the Default RDF dataset\n const dataset = new HashMapDataset(GRAPH_A_IRI, graph_a)\n\n // insert graph_b as a Named Graph\n dataset.addNamedGraph(GRAPH_B_IRI, graph_b)\n```\n\n## Running a SPARQL query\n\nFinally, to run a SPARQL query on your RDF dataset, you need to use the `PlanBuilder` class. It is responsible for parsing SPARQL queries and building a pipeline of iterators to evaluate them.\n\n```javascript\n  const { PlanBuilder } = require('sparql-engine')\n\n  // Get the name of all people in the Default Graph\n  const query = `\n    PREFIX foaf: <http://xmlns.com/foaf/0.1/>\n    SELECT ?name\n    WHERE {\n      ?s a foaf:Person .\n      ?s foaf:name ?name .\n    }`\n\n  // Creates a plan builder for the RDF dataset\n  const builder = new PlanBuilder(dataset)\n\n  // Get an iterator to evaluate the query\n  const iterator = builder.build(query)\n\n  // Read results\n  iterator.subscribe(\n    bindings => console.log(bindings),\n    err => console.error(err),\n    () => console.log('Query evaluation complete!')\n  )\n```\n\n# Enable caching\n\nThe `sparql-engine` provides support for automatic caching of Basic Graph Pattern evaluation using the [Semantic Cache algorithm](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=1161590). Basically, the cache will save the results of BGPs already evaluated and, when the engine wants to evaluates a BGP, it will look for the largest subset of the BGP in the cache. If one is available, it will re-use the cached results to speed up query processing.\n\nBy default, semantic caching is disabled. You can turn it on/off using the `PlanBuilder.useCache` and `PlanBuilder.disableCache` methods, respectively. The `useCache` method accepts an optional parameter, so you can provide your own implementation of the semantic cache. By defaults, it uses an in-memory [LRU cache](https://callidon.github.io/sparql-engine/classes/lrubgpcache.html) which stores up to 500MB of items for 20 minutes.\n\n```javascript\n// get an instance of a PlanBuilder\nconst builder = new PlanBuilder(/* ... */)\n\n// activate the cache\nbuilder.useCache()\n\n// disable the cache\nbuilder.disableCache()\n```\n\n# Full Text Search\n\nThe `sparql-engine` provides a non-standard full text search functionnality,\nallowing users to execute [approximate string matching](https://en.wikipedia.org/wiki/Approximate_string_matching) on RDF Terms retrieved by SPARQL queries.\nTo accomplish this integration, it follows an approach similar to [BlazeGraph](https://wiki.blazegraph.com/wiki/index.php/FullTextSearch) and defines several **magic predicates** that are given special meaning, and when encountered in a SPARQL query, they are interpreted as configuration parameters for a full text search query.\n\nThe simplest way to integrate a full text search into a SPARQL query is to use the magic predicate `ses:search` inside of a SPARQL join group. In the following query, this predicate is used to search for the keywords `neil` and `gaiman` in the values binded to the `?o` position of the triple pattern.\n```\nPREFIX foaf: <http://xmlns.com/foaf/0.1/>\nPREFIX ses: <https://callidon.github.io/sparql-engine/search#>\nSELECT * WHERE {\n  ?s foaf:knows ?o .\n  ?o ses:search “neil gaiman” .\n}\n```\nIn a way, full text search queries allows users to express more complex SPARQL filters that performs approximate string matching over RDF terms.\nEach result is annotated with a *relevance score* (how much it matches the keywords, higher is better) and a *rank* (they represent the descending order of relevance scores). These two values are not binded by default into the query results, but you can use magic predicates to get access to them (see below). Note that the meaning of relevance scores is specific to the implementation of the full text search.\n\nThe full list of magic predicates that you can use in a full text search query is:\n* `ses:search` defines keywords to search as a list of keywords separated by spaces.\n* `ses:matchAllTerms` indicates that only values that contain all of the specified search terms should be considered.\n* `ses:minRelevance`and `ses:maxRelevance` limits the search to matches with a minimum/maximum\nrelevance score, respectively. In the default implementation, scores are floating numbers, ranging from 0.0 to 1.0 with a precision of 4 digits.\n* `ses:minRank` and `ses:maxRank` limits the search to matches with a minimum/maximum\nrank value, respectively. In the default implementation, ranks are positive integers starting at 0.\n* `ses:relevance` binds each term's relevance score to a SPARQL variable.\n* `ses:rank` binds each term's rank to a SPARQL variable.\n\nBelow is a more complete example, that use most of these keywords to customize the full text search.\n```\nPREFIX foaf: <http://xmlns.com/foaf/0.1/>\nPREFIX ses: <https://callidon.github.io/sparql-engine/search#>\nSELECT ?s ?o ?score ?rank WHERE {\n  ?s foaf:knows ?o .\n  ?o ses:search “neil gaiman” .\n  ?o ses:minRelevance “0.25” .\n  ?o ses:maxRank “1000” .\n  ?o ses:relevance ?score .\n  ?o ses:rank ?rank .\n  ?o ses:matchAllTerms “true” .\n}\n```\n\nTo provide a custom implementation for the full text search that is more integrated with your backend,\nyou simply need to override the `fullTextSearch` method of the `Graph` class.\nYou can find the full signature of this method in the [relevant documentation](https://callidon.github.io/sparql-engine/classes/graph.html#fullTextSearch).\n\nThe `sparql-engine` framework provides a default implementation of this method, which computes relevance scores as the average ratio of keywords matched by words in the RDF terms.\nNotice that **this default implementation is not suited for production usage**.\nIt will performs fine for small RDF datasets, but, \nwhen possible, you should always provides a dedicated implementation that leverages your backend.\nFor example, for SQL databases, you could use [GIN or GIST indexes](https://www.postgresql.org/docs/12/gin-intro.html).\n\n# Federated SPARQL Queries\n\nThe `sparql-engine` framework provides automatic support for evaluating [federated SPARQL queries](https://www.w3.org/TR/2013/REC-sparql11-federated-query-20130321/), using the [`SERVICE` keyword](https://www.w3.org/TR/sparql11-query/#basic-federated-query).\n\nTo enable them, you need to set **a Graph Factory** for the RDF dataset used to evaluate SPARQL queries.\nThis Graph factory is used by the dataset to create new RDF Graph on-demand.\nTo set it, you need to use the [`Dataset.setGraphFactory`](https://callidon.github.io/sparql-engine/classes/dataset.html#setgraphfactory) method, as detailed below.\nIt takes *a callback* as parameter, which will be invoked to create a new graph from an IRI.\nIt's your responsibility to define the graph creation logic, depending on your application.\n\n```typescript\nconst { HashMapDataset } = require('sparql-engine')\nconst CustomGraph = require(/* import your Graph subclass */)\n\nconst my_graph = new CustomGraph(/* ... */)\n\nconst dataset = new HashMapDataset('http://example.org#graph-a', my_graph)\n\n// set the Graph factory of the dataset\ndataset.setGraphFactory(iri => {\n  // return a new graph for the provided iri\n  return new CustomGraph(/* .. */)\n})\n```\n\nOnce the Graph factory is set, you have nothing more to do!\nJuste execute your federated SPARQL queries as regular queries, like before!\n\n# Custom Functions\n\nSPARQL allows custom functions in expressions so that queries can be used on domain-specific data.\nThe `sparql-engine` framework provides a supports for declaring such custom functions.\n\nA SPARQL value function is an extension point of the SPARQL query language that allows URI to name a function in the query processor.\nIt is defined by an `IRI` in a `FILTER`, `BIND` or `HAVING BY` expression.\nTo register custom functions, you must create a JSON object that maps each function's `IRI` to a Javascript function that takes a variable number of **RDF Terms** arguments and returns one of the following:\n* A new RDF Term (an IRI, a Literal or a Blank Node) in RDF.js format.\n* An array of RDF Terms.\n* An [Iterable](https://developer.mozilla.org/en-US/docs/Web/JavaScript/Reference/Iteration_protocols) or a [Generator](https://developer.mozilla.org/en-US/docs/Web/JavaScript/Reference/Global_Objects/Generator) that yields RDF Terms.\n* The `null` value, to indicates that the function's evaluation has failed.\n\nRDF Terms are represented using the [RDF.js data model](http://rdf.js.org/data-model-spec/).\nThe [`rdf` subpackage](https://callidon.github.io/sparql-engine/modules/rdf.html) exposes a lot\nof utilities methods to create and manipulate RDF.js terms in the context of custom SPARQL functions.\n\nThe following shows a declaration of some simple custom functions.\n```javascript\n// load the utility functions used to manipulate RDF terms\nconst { rdf } = require('sparql-engine')\n\n// define some custom SPARQL functions\nconst customFunctions = {\n  // reverse a RDF literal\n  'http://example.com#REVERSE': function (rdfTerm) {\n    const reverseValue = rdfTerm.value.split(\"\").reverse().join(\"\")\n    return rdf.shallowCloneTerm(rdfTerm, reverseValue)\n  },\n  // Test if a RDF Literal is a palindrome\n  'http://example.com#IS_PALINDROME': function (rdfTerm) {\n    const result = rdfTerm.value.split(\"\").reverse().join(\"\") === rdfTerm.value\n    return rdf.createBoolean(result)\n  },\n  // Test if a number is even\n  'http://example.com#IS_EVEN': function (rdfTerm) {\n    if (rdf.termIsLiteral(rdfTerm) && rdf.literalIsNumeric(rdfTerm)) {\n      const jsValue = rdf.asJS(rdfTerm.value, rdfTerm.datatype.value)\n      const result = jsValue % 2 === 0\n      return rdf.createBoolean(result)\n    }\n    return terms.createFalse()\n  }\n}\n```\n\nThen, this JSON object is passed into the constructor of your PlanBuilder.\n\n```javascript\nconst builder = new PlanBuilder(dataset, {}, customFunctions)\n```\n\nNow, you can execute SPARQL queries with your custom functions!\nFor example, here is a query that uses our newly defined custom SPARQL functions.\n\n```\nPREFIX foaf: <http://xmlns.com/foaf/0.1/>\nPREFIX example: <http://example.com#>\nSELECT ?length\nWHERE {\n  ?s foaf:name ?name .\n\n  # this bind is not critical, but is here for illustrative purposes\n  BIND(<http://example.com#REVERSE>(?name) as ?reverse)\n\n  BIND(STRLEN(?reverse) as ?length)\n\n  # only keeps palindromes\n  FILTER (!example:IS_PALINDROME(?name))\n}\nGROUP BY ?length\nHAVING (example:IS_EVEN(?length))\n```\n\n# Advanced usage\n\n## Customize the pipeline implementation\n\nThe class `PipelineEngine` (and its subclasses) is the main component used by `sparql-engine` to evaluate all SPARQL operations. It defines basic operations (`map`, `filter`, etc) that can be used\nto manipulate intermediate results and evaluate SPARQL queries.\n\nBy default, the framework uses an implementation of `PipelineEngine` based on [`rxjs`](https://rxjs-dev.firebaseapp.com/), to implements a SPARQL query execution plan as a pipeline of iterators.\nHowever, **you are able to switch to others implementations** of `PipelineEngine`, using `Pipeline.setInstance`.\n\n```javascript\nconst { Pipeline, PipelineEngine } = require('sparql-engine')\n\nclass CustomEngine extends PipelineEngine {\n  // ...\n}\n\n// add this before creating a new plan builder\nPipeline.setInstance(new CustomEngine())\n// ...\n```\n\nTwo implementations of `PipelineEngine` are provided by default.\n* `RxjsPipeline`, based on [`rxjs`](https://rxjs-dev.firebaseapp.com/), which provides a pure pipeline approach. This approach is **selected by default** when loading the framework.\n* `VectorPipeline`, which materializes all intermediate results at each pipeline computation step. This approach is more efficient CPU-wise, but also consumes a lot more memory.\n\nThese implementations can be imported as follows:\n```javascript\nconst { RxjsPipeline, VectorPipeline } = require('sparql-engine')\n```\n\n## Customize query execution\n\nA `PlanBuilder` implements a [Builder pattern](https://en.wikipedia.org/wiki/Builder_pattern) in order to create a physical query execution plan for a given SPARQL query.\nInternally, it defines [*stages builders*](https://callidon.github.io/sparql-engine/classes/stagebuilder) to generates operators for executing all types of SPARQL operations.\nFor example, the [`OrderByStageBuilder`](https://callidon.github.io/sparql-engine/classes/orderbystagebuilder.html) is invoked when the `PlanBuilder` needs to evaluate an `ORDER BY` modifier.\n\nIf you want to customize how query execution plans are built, you have to implement your own stage builders, by extending existing ones.\nThen, you need to configure your plan builder to use them, with the [`use` function](https://callidon.github.io/sparql-engine/classes/planbuilder.html#use).\n\n```javascript\n  const { PlanBuilder, stages } = require('sparql-engine')\n\n  class MyOrderByStageBuilder extends stages.OrderByStageBuilder {\n    /* Define your custom execution logic for ORDER BY */\n  }\n\n  const dataset = /* a RDF dataset */\n\n  // Creates a plan builder for the RDF dataset\n  const builder = new PlanBuilder(dataset)\n\n  // Plug-in your custom stage builder\n  builder.use(stages.SPARQL_OPERATION.ORDER_BY, MyOrderByStageBuilder(dataset))\n\n  // Now, execute SPARQL queries as before with your PlanBuilder\n```\n\nYou will find below a reference table of all stage builders used by `sparql-engine` to evaluate SPARQL queries. Please see [the API documentation](https://callidon.github.io/sparql-engine/classes/stagebuilder) for more details.\n\n**Executors**\n\n| SPARQL Operation | Default Stage Builder | Symbol |\n|------------------|-----------------------|--------|\n| [Aggregates](https://www.w3.org/TR/sparql11-query/#aggregates) | [AggregateStageBuilder](https://callidon.github.io/sparql-engine/classes/aggregatestagebuilder.html) | `SPARQL_OPERATION.AGGREGATE` |\n| [Basic Graph Patterns](https://www.w3.org/TR/sparql11-query/#BasicGraphPatterns) | [BGPStageBuilder](https://callidon.github.io/sparql-engine/classes/bgpstagebuilder.html) | `SPARQL_OPERATION.BGP` |\n| [BIND](https://www.w3.org/TR/sparql11-query/#bind) | [BindStageBuilder](https://callidon.github.io/sparql-engine/classes/bindstagebuilder.html) | `SPARQL_OPERATION.BIND` |\n| [DISTINCT](https://www.w3.org/TR/sparql11-query/#neg-minus) | [DistinctStageBuilder](https://callidon.github.io/sparql-engine/classes/distinctstagebuilder.html) | `SPARQL_OPERATION.DISTINCT` |\n| [FILTER](https://www.w3.org/TR/sparql11-query/#expressions) | [FilterStageBuilder](https://callidon.github.io/sparql-engine/classes/filterstagebuilder.html) | `SPARQL_OPERATION.FILTER` |\n| [Property Paths](https://www.w3.org/TR/sparql11-query/#propertypaths) | [PathStageBuilder](https://callidon.github.io/sparql-engine/classes/pathstagebuilder.html) | `SPARQL_OPERATION.PROPERTY_PATH` |\n| [GRAPH](https://www.w3.org/TR/sparql11-query/#rdfDataset) | [GraphStageBuilder](https://callidon.github.io/sparql-engine/classes/graphstagebuilder.html) | `SPARQL_OPERATION.GRAPH` |\n| [MINUS](https://www.w3.org/TR/sparql11-query/#neg-minus) | [MinusStageBuilder](https://callidon.github.io/sparql-engine/classes/minusstagebuilder.html) | `SPARQL_OPERATION.MINUS` |\n| [OPTIONAL](https://www.w3.org/TR/sparql11-query/#optionals) | [OptionalStageBuilder](https://callidon.github.io/sparql-engine/classes/optionalstagebuilder.html) | `SPARQL_OPERATION.OPTIONAL` |\n| [ORDER_BY](https://www.w3.org/TR/sparql11-query/#modOrderBy) | [OrderByStageBuilder](https://callidon.github.io/sparql-engine/classes/orderbystagebuilder.html) | `SPARQL_OPERATION.ORDER_BY` |\n| [SERVICE](https://www.w3.org/TR/sparql11-query/#basic-federated-query) | [ServiceStageBuilder](https://callidon.github.io/sparql-engine/classes/servicestagebuilder.html) | `SPARQL_OPERATION.SERVICE` |\n| [UNION](https://www.w3.org/TR/sparql11-query/#alternatives) | [UnionStageBuilder](https://callidon.github.io/sparql-engine/classes/unionstagebuilder.html) | `SPARQL_OPERATION.UNION` |\n| [UPDATE](https://www.w3.org/TR/2013/REC-sparql11-update-20130321/) | [UpdateStageBuilder](https://callidon.github.io/sparql-engine/classes/updatestagebuilder.html) | `SPARQL_OPERATION.UPDATE` |\n\n\n# Documentation\n\nTo generate the documentation in the `docs` director:\n```bash\ngit clone https://github.com/Callidon/sparql-engine.git\ncd sparql-engine\nyarn install\nnpm run doc\n```\n\n# Acknowledgments\n\nThis framework is developed since 2018 by many contributors, and we thanks them very much for their contributions to this project! Here is the full list of our amazing contributors.\n\n* [Corentin Marionneau](https://github.com/Slaanaroth) (@Slaanaroth)\n  * Corentin created the first version of `sparql-engine` during its research internship at the [Laboratoire des Sciences du Numérique de Nantes](https://www.ls2n.fr/) (LS2N). He is now a Web developer at SII Atlantique.\n* [Merlin Barzilai](https://github.com/Rintarou) (@Rintarou)\n  * Merlin designed the first SPARQL compliance tests for the framework during its research internship at the [LS2N](https://www.ls2n.fr/).\n* [Dustin Whitney](https://github.com/dwhitney) (@dwhitney)\n  * Dustin implemented the support for custom SPARQL functions and provided a lot of feedback during the early stages of development.\n* [Julien Aimonier-Davat](https://github.com/Lastshot97) (@Lastshot97)\n  * Julien implemented the support for SPARQL Property Paths evaluation during its research internship at the [LS2N](https://www.ls2n.fr/). He is now a Ph.D. Student at the University of Nantes.\n* [Arnaud Grall](https://github.com/folkvir) (@folkvir)\n  * Arnaud contributed to many bugfixes and provided a lot of feedback throughout the development of the framework. He is now a Software Engineer at SII Atlantique.\n* [Thomas Minier](https://github.com/Callidon) (@Callidon)\n  * Thomas developed the framework during his PhD thesis in the [Team \"Gestion des Données Distribuées\"](https://sites.google.com/site/gddlina/) (GDD) and supervise its evolution ever since. He is now a Software Engineer at SII Atlantique.\n\n# References\n\n* [Official W3C RDF specification](https://www.w3.org/TR/rdf11-concepts)\n* [Official W3C SPARQL specification](https://www.w3.org/TR/2013/REC-sparql11-query-20130321/)\n","readmeFilename":"README.md"}