Tree structured fragmentations of an RDF data collection.

This project handles the creation of tree structured fragmentations of a collection of RDF data.
This is based on the ontology that can be found here: https://github.com/pietercolpaert/TREE.

Tree structured fragmentations of an RDF data collection provide a way for clients to filter the data objects of a collection over the Web with a minimal server cost.
For every predicate of the stored data that needs to be filtered, a tree data structure has to be constructed that indexes the data over that predicate value.
If these fragmentations are published over the Web, clients can use them to efficiently filter the data objects of the data set over the predicates for which tree structured fragmentations are available.
A client implementation can be found here: https://github.com/Dexagod/ldf_tree_client.

When publishing the tree structured fragmentations over the Web, it is important to enable caching on both the server and the client, as this will increase the querying performance of the clients over the fragmentation.

Currently, four data structure types are supported: Prefix tree, R-tree, B-tree and Paged List (Hydra PartialCollectionView).
The Hydra collectionView does not scale well for large datasets as the creation currently iterates over each page in order to add an item to the list.

A tree can be setup the following way:
``import * as ldtree from "linkeddatatree"

let config = { rootDir : 'rootDir/', // This is the root of all @id values in the tree dataDir : 'hydra_streets/', // This is the relative path to the root where the fragments of the tree are stored treePath: 'rdf:label', // This is the tree:path fragmentSize: 100 // The maximum size of a fragment of the tree (defaults to 50) memoryLimit: 1000 // The maxumum memory size that can be used for the creation of the tree (in MB, defaults to Infinity). Currently not yet correctly implemented.'writeMetadata': true // Flag to allow metadata to be written with the tree fragments (Allows the tree to be read for later adaptations, will be made obsolete in later iterations) 'context': { // JSON-LD context to be added to the tree fragment. "rdf": "http://www.w3.org/1999/02/22-rdf-syntax-ns#", } }

// Currently there are 3 available tree data structures (and support for Hydra PartialCollectionView) let treeManager = new ldtree.PrefixTreeManager() // = new ldtree.BTreeManager() // = new ldtree.RTreeManager() // = new ldtree.HydraPartialCollectionViewManager()

// Creating the tree object let tree = treeManager.createTree(config);

// Creating a data object to add to the tree let key_value = "Wolfgang Amadeus Mozart" // This key value is a temporary solution. In following iterations, this will be derived from the RDF data object based on the shacl_path predicate that has been provided on the creation of the tree. // Currently, data objects added to the tree are required to be presented in a JSON-LD format. let data_object = { "@id": "http://dbpedia.org/resource/Wolfgang_Amadeus_Mozart", "@type": "http://dbpedia.org/ontology/Person", "http://www.w3.org/2000/01/rdf-schema#label": "Wolfgang Amadeus Mozart"}

tree.addData(key_value, data_object) // The key_value parameter will be removed in later iterations, and will be derived from the RDF data object based ont he shacl_path predicate value. // Add all the necessary data to the tree data structure. tree.doneAdding() // This flushes the cache to the disk, so that the current status of the tree structured fragmentation is written to the disk.

// The fragments of the tree have now been written to the disk on the path: source_path + tree_path // The collection object in the root node fragment can be found on the location: source_path + tree_path + node0.jsonld#Collection

// The tree can be read from disk to make alterations using the following approach let newtree = treeManager.readTree( source_path, tree_path, shacl_path, // In future itearations this will be derived from context, max_cached_fragments, // In future itearations this will be derived from context. max_fragment_size) // In future itearations this will be derived from context.

newtree.addData(key_value2, data_object20 // add new items to the tree

newtree.doneAdding() // This flushes the tree cache to disk after the creation of the tree. This creates a config file that can be used by the program to read the tree from the disk in order to make adaptations.